PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases.Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 , to introduce a strategy that both helps find genetic associations and proposes biological hypotheses for the mechanisms behind those associations. To achieve this, we use large genetic studies carried out on traits that are related to OSA, and look for genetic variants that affect both OSA in our small study and the trait in question in the large study. By linking two or more traits at select loci, we were able to, among other results, find a locus that affects the expression of a gene in immune cells (DNA primase subunit 1), a marker of thrombotic and inflammatory processes (plateletcrit) and OSA. This results in a novel genetic association to OSA and a corresponding biological hypothesis behind its effect on OSA. Genome-wide association studies of human phenotypes ranging from gene expression to human diseases are now routine. Cumulatively, the data indicate that complex traits are highly polygenic ,2, and gOSA is characterized by recurrent episodes of partial or complete obstruction of the pharyngeal airway resulting in multiple physiological disturbances, including sympathetic nervous system activation, increased energy cost of breathing, intermittent hypoxemia, and wide swings in intrathoracic pressure. This disorder is highly prevalent in the general population, affecting more than 10% of middle-aged adults, with increased prevalence observed with aging, obesity, and cardiometabolic disease, and is more common in men . OSA lea2 and percentage of sleep with SpO2 < 90%, and variants in a locus on 2q12 were associated with minimum oxygen saturation (SpO2). In another study, we identified a locus in 17p11 with a male-specific effect on AHI [2 < 90% [Indices of OSA, including the Apnea-Hypopnea Index , apnea event duration, indices of overnight hypoxemia, habitual snoring, and excessive daytime sleepiness, show substantial heritability in family studies . Past stt on AHI . Further[2 < 90% .The low number of genetic associations reported to date only explains a small fraction of OSA trait heritability. This relative paucity of findings is driven primarily by modest sample sizes, a reflection of the expense and complexity of measuring physiological phenotypes by overnight polysomnography or respiratory polygraphy. This also limits our ability to fine-map associations down to causative variants and thus identify relevant genes. Data on hundreds of thousands of individuals–sample sizes at which GWAS designs are well-powered to detect tens of loci and, in combination with additional experiments, fine-map some of them–have yet to be collected for OSA traits based on overnight polysomnography or respiratory polygraphy and may never be available ,2. BioloNow that GWAS of massive sample sizes have been accumulated for various comorbid conditions and endophenotypes related to OSA, we hypothesize that analysis of shared associations across correlated traits can identify effects in underpowered studies of OSA and generate clear and testable biological hypotheses. A number of computational methods increase power for discovering genetic associations by capitalizing on pleiotropy between disease phenotypes or between a disease and a molecular trait such as gene expression. One common approach takes advantage of the genetic correlation among phenotypes ,29–31. TIn this study, we apply a colocalization method to detect shared associations between OSA and other related traits. We elected Joint Likelihood Mapping method (JLIM)  and eCAVBy linking different clinical and gene expression traits to OSA at specific loci, our analysis suggests new hypotheses about OSA pathobiology across many physiological layers as well as finding a new association.p < 5 x 10−8) in our well-powered trait , and from these we selected the subsets which also show nominal association to OSA traits (p < 0.01 for any SNP in the locus). We then used colocalization to directly evaluate if the association to the two traits was consistent with the same underlying effect, indicating a pleiotropic effect. This two-step strategy allowed us to distinguish between cases where there was association only in the clinical trait; where there was a shared association in both traits; and if there were distinct associations in both traits stemming from different underlying effects , where the same variant is causative for the association to both traits (H1), and where two distinct variants in LD drive each of the associations at a locus (H2) (Methods). To simulate summary statistics from a well-powered GWAS , we sampled values from a multivariate normal (MVN) distribution using the local LD matrix as a variance-covariance parameter [1). For example, in the first case, we simulated 2,500 loci with 3.5% corresponding to H1, 66.5% corresponding to H2 and 30% corresponding to no association in the underpowered trait (H0). The focus of our simulation study was the ability to replicate the association signal for the primary phenotype in an independent cohort. This means successfully discriminating between (H1+H2) and H0. We leave the interpretability question (discriminating H1 from H2) to Discussion.We first used simulations to assess our strategy to identify true associations in an underpowered study by detecting pleiotropic associations with a better-powered study of another trait and following up with replication in an independent cohort. To assess sensitivity and specificity, we simulated variable proportions of shared and distinct causal effects, whilst assuming high polygenicity for each trait and only one causative variant per locus. We simulated GWAS statistics for pairs of traits corresponding to our own study design: we simulated situations where no variant is causative for an underpowered trait : . At the same time, almost all proportions of successfully replicated associations are driven by the same variant (H1) in our simulation regimes  ,39. We ames Figs .2 from the analysis  in the discovery sample, it substantially increases the rate of successful replication. The main reason for the increase in the rate of successful replication is the prioritization of H1 loci of larger true effect size and the corresponding reduction of multiple testing burden at the replication stage. The benefit of this approach increases with the fraction of loci driven by distinct effects (H2) (0) is low  and cFDR are using different features of the data, we found that taking a consensus between them can effectively exclude the majority of Hanalysis . In the % are H2 . Even thcts (H2) . It alsogenicity  S6 Fig)2 from thp-value threshold of cFDR to select the same number of candidates in discovery as the consensus method . For the additional well-powered traits, we randomly decided whether the simulated causative variant was to be the same as or distinct from that of the main well-powered trait. Using this simulated dataset, we examined the power of the above Bayesian methods and non-parametric meta-analysis (iGWAS) [Next, we compared the rate of successful replication between the consensus approach and Bayesian meta-analysis methods. In contrast to colocalization methods, the Bayesian meta-analysis approach leverages the correlation of effect sizes between traits rather than SNP-level colocalization of causative variants –31. We c (iGWAS)  in multiFinally, we found that looking at more trait combinations with more GWAS peaks results in proportionally more discoveries . Overall2) during sleep [Based on clinical relevance  and hering sleep . We usedWe assembled a collection of GWAS summary statistics for a total of 55 candidate intermediate traits from across these physiological areas: erythroid, leukocyte and platelet counts and function, from a study combining the UK Biobank and INTERVAL datasets  ,57, and p < 5 x 10−8) in the 55 clinical traits, of which 2,939 had a corresponding suggestive association to one of the four OSA traits  . Due to 2 during sleep. It is an intronic variant in the hexokinase 1 (HK1) region in chromosome 10, and has been previously reported, as it reached genome-wide significance in association to minimum and average SpO2 [PRIM1) in chromosome 12. It links plateletcrit  to AHI.2 levels, and abnormalities in upper airway muscle function are fundamental mechanisms for sleep apnea [p-values over the 5,860 comparisons against 1,009 protein-coding genes of which transcription start sites are within 1 Mb from the index SNPs of clinical traits.Non-coding regions with evidence of gene regulatory activity carry a large proportion of heritability in most traits analyzed in large GWAS . We reasep apnea ,65; the ep apnea . We calcPRIM1 but also an eQTL for PRIM1 levels in monocytes and T cells  expression in lung tissue is pleiotropic with a measure of lung function , which in turn is pleiotropic with an association with minimum SpO2 during sleep (MMP15 (rs4784886) is marginally associated with minimum SpO2 in the validation cohort . Overall, of the eight identified genes, four genes  are known to be induced by hypoxia and oxidative stress, and three genes  are implicated in inflammatory responses. The colocalization of associations to gene expression in specific cell types, clinical and sleep apnea traits suggests biological hypotheses of the pathophysiology underlying OSA.We were able to identify shared associations between eQTL and clinical traits in 7/61 loci  colocalizes with expression of the epsilon subunit of the nicotinic receptor (CHRNE), in various tissues, including neutrophils, monocytes, spleen and muscle  and is associated with average overnight oxygen saturation level (SpO2). This locus is pleiotropic with most of the red blood cell related traits tested . Lowered oxygen carrying capacity and thus more tissue hypoxia could also contribute to breathing instability (and thus apneas) via Hypoxia-Inducible Factor-1 (HIF-1) and enhanced carotid body sensitivity and chemoreflex activation, or through long-term respiratory facilitation and plasticity [In our comparison of clinical  traits to OSA-related traits, the strongest finding lies in an intronic region of hexokinase 1 . We found that a missense variant in PRIM1 colocalizes with its own expression levels in monocytes and T cells, plateletcrit and AHI. The association of this variant with AHI was replicated in an independent validation cohort. The PRIM1 deficiency is known to cause lymphopenia along with severe growth retardation, microcephaly and “triangular face” [PRIM1 on craniofacial morphology may contribute to OSA by causing a narrow airway, and thereby increasing the risk of airway obstruction during sleep; however, an inflammatory mechanism is also possible.The analysis of pleiotropy can be used to concatenate more than one phenotype to create candidate “causal chains,” which by linking eQTLs to well-powered traits to sleep apnea related traits can hint at promising biological targets. Among the most significant results for this multicomponent model is DNA primase subunit 1 (ar face” . The impMMP15), a gene whose expression in lung tissue is affected by an eQTL that colocalizes with a lung function phenotype (FEV1/FVC) which itself colocalizes with minimum SpO2 during sleep. Matrix metallopeptidases (MMPs), a family of proteolytic enzymes that can be activated by inflammation and oxidative stress, participate in and potentiate tissue remodeling by breaking down the extracellular matrix. MMPs have been suggested to play an etiological role in OSA-related pathophysiological responses that may lead to multiple organ dysfunction [MMP15 (membrane-type 2 MMP) is known to be upregulated by HIF-1α under hypoxic conditions [Another example of causal chains is matrix metallopeptidase 15  eQTL in T cells colocalizes with associations to the lymphocyte count, AHI as well as oxygen saturation. While Gasdermin A is considered to be expressed mostly in epithelial cells rather than in T cells, broadly, gasdermins mediate inflammatory responses via pyroptosis, a form of programmed cell death leading to the release of proinflammatory molecules, and play a key role in NLRP-inflammasome responses. Increased level of gasdermin D has been shown to mediate hypoxia-related muscle injury in an animal model of OSA [We also found that l of OSA . Inflamml of OSA .CHRNE that colocalizes with a genome-wide significant association in minimum oxygen saturation. This receptor is present at neuromuscular junctions, and mutations in this subunit are known to cause congenital myasthenic syndrome in humans that can result in progressive respiratory impairment [Another interesting result is an eQTL in the epsilon subunit of the nicotinic acetylcholine receptor pairment . As discHK1 locus. We could not identify two other known OSA associations at 2q12 and 17p11 using this approach, as there is no apparent pleiotropy between OSA and our library of clinical traits. Adding gene expression traits to this analysis increases our discovery ability, as we can identify the 17p11 association as pleiotropic with an eQTL for CHRNE in neutrophils, monocytes, spleen and muscle  from our initial OSA GWAS in individuals of European ancestry. We focused on AHI since we detected the most SNPs reaching nominal replication in this trait. We found that 11,910 of these would have been selected for our colocalization analysis, in line with expectation for the presence of a SNP within the 100kb distance satisfying our in-sample threshold of association p < 0.01. Out of these, 6,123 had a significant out-of-sample nominal replication (association p < 0.05). In comparison, our pleiotropy analysis results for AHI are 4.6-fold enriched at this threshold (4/20 independent hits with r2 < 0.1), suggesting the presence of true associations to sleep traits (one-sided Fisher Exact Test p = 0.018). In fact, the 76/975 associations that did not show significant evidence of pleiotropy were also slightly enriched for nominal replication relative to the set of randomly selected variants . This suggests that additional pleiotropic effects–and therefore true OSA associations–remain to be discovered above the FDR cutoff we applied to our colocalization results although we cannot rule out the possibility that the random SNPs may not fully recapitulate the functional properties of GWAS SNPs from clinical traits.Replication in an independent cohort is frequently necessary to validate GWAS findings in small discovery samples. In this study, we significantly replicate the association of two loci to OSA after Bonferroni correction and identify additional eight loci that replicate only nominally but are probably enriched with true positive associations to OSA . We rand2 loci, respectively, as driven by the same variant . JLIM estimates accurately calibrated p-values under H0 and conservatively approximate p-values under H2. For underpowered GWAS studies, with limited sample sizes, we lowered JLIM’s genetic resolution parameter θ to 0.5 in this study. JLIM does not attempt to distinguish distinct causative variants beyond its specified genetic resolution from the same causal effect. When distinct causative variants for two traits are separated with LD r2 < 0.5, JLIM maintains the ability to distinguish the same and distinct effects in simulations .To study pleiotropic associations underlying the risk of OSA, we prepared two sets of cohorts: the discovery cohorts to identify pleiotropic variants and independent replication cohorts to validate their associations to OSA traits. For the discovery cohorts, we used individual-level genotype data in order to determine the significance of pleiotropy by permutation (JLIM) . At the The discovery cohorts included the subset of samples of European ancestry from the following five cohorts: the Atherosclerosis Risk in Communities Study (ARIC) , OsteopoThe replication cohorts were: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) ,89, Star2), apnea-hypopnea index (AHI) and event duration. Briefly, the minimum and average SpO2 were calculated from oximetry-based SpO2 measurements over the entire recorded sleep interval excluding occasional waking periods. AHI was scored by counting the number of episodes of complete (apnea) or partial (hypopneas) airflow reduction associated with ≥ 3% desaturation per hour of sleep. The event duration was measured for the average length of apneas and hypopneas, from the nadir of the first reduced breath to the nadir following the last reduced breath (in seconds). The full description of phenotyping protocols is present in the original studies which first reported their genetic analysis in the context of OSA [We examined the following four OSA-related traits in the discovery and replication cohorts: minimum and average oxygen saturation . In these loci, JLIM compares the likelihood of observed association signals under the following three competing possibilities: the OSA trait has no causative variant in the locus (“H0”), the same OSA causative variant is shared with the clinical trait (“H1”), and the OSA causative variant is distinct from the clinical trait causative variant (“H2”), as shown in 1 and H2. r2>θ), and r2<θ). In our simulation of underpowered GWAS (see below for the details), only a small fraction (9.0%) of loci had lead SNPs in tight LD (r2>0.8) with causative SNPs; only 15.7% of loci had the modest LD of r2>0.5 between lead and causative SNPs. This contrasts with well-powered GWAS data, for which, in 91.4% of simulated loci, lead SNPs were in LD of r2>0.8 with causative SNPs. To account for this limited statistical resolution of underpowered GWAS data, we lowered θ to 0.5 from the default of 0.8 in this study.We applied Joint Likelihood Mapping (JLIM version 2.0)  to test 0. The false positives due to H0 as previously shown analytically [p-value estimated under H0 can be used to control for JLIM calculates the ratio between the likelihood of the data under ytically . Thus, uments of  for the θ to 0.5 from the default value of 0.8. All other parameters were set to the default. We ran JLIM only in the discovery cohorts.To run JLIM, we used the genetic association statistics of OSA traits calculated over all common SNPs in a 200kb analysis window around the focal SNP . We derived these statistics from our discovery cohorts by combining association signals of each cohort using an inverse-variance weighted meta-analysis approach. The association statistics were calculated in individual cohorts by linear regression adjusting for age, sex, BMI and the top three principal components. The principal components were calculated from genome-wide genotype data in each cohort separately. We used mean imputation for missing covariate values. Multi-allelic SNPs and rare variants with minor allele frequencies (MAF) below 0.05 were excluded from the analysis. We only used variants present in all of the discovery cohorts. To reflect the limited resolution for fine-mapping of causative variants in underpowered studies, we relaxed JLIM’s genetic resolution parameter p-values even under the vagaries of imputation noise across cohorts for underpowered traits. For the permutation procedure, we used the same pipeline described above to generate permuted association statistics for JLIM. OSA phenotypes were randomly shuffled in each cohort separately. For each permutation, the association statistics were calculated in the same way including all the covariates. Then, the cohort-level association statistics calculated on permuted data were combined across cohorts by meta-analysis. This permutation procedure was repeated up to 100,000 times, adaptively, to estimate JLIM p-values.JLIM 2.0 requires individual-level genetic data for underpowered traits in order to run permutations. This limits the applicability of our approach to more general cases where only summary-level data are available. The direct permutation on individual-level genetic data, however, enables robust estimation of u and v indicate a combination of clinical and OSA traits, JLIMu,v(l) is the JLIM p-value of tested locus l, and Nu,v is the number of loci tested between the trait pairs u and v. JLIM hits were obtained at the FDR threshold of 0.2. The JLIM/cFDR consensus was defined by the intersection between the JLIM (FDR < 0.2) and cFDR hits defined at the same p-value threshold. Specifically, given the JLIM p-value cutoff of FDR 0.2, i.e., p0.2 such that FDRu,v(p0.2) = 0.2, the cFDR hits were defined by the association p-value below p0.2 for an OSA trait. Note that the p-value of association to a clinical trait is always ascertained to be less than 5 x 10−8 in our analysis. The cFDR association p-values were examined at the focal SNP.We accounted for the multiple testing burden with the False Discovery Rate (FDR), separately for each clinical and OSA trait combination. Specifically, we used the Benjamini-Hochberg procedure to calculate the FDR as follows:p-value cutoffs using unfiltered null simulation datasets (H0), which were not preconditioned on the minimum association p-values in loci.eCAVIAR (version 2.2) was run in the default setting. The reference LD matrix was set to the LD estimated from the subjects of European ancestry  randomly subsampled out of our OSA discovery cohort. Colocalization Posterior Probability (CLPP) was calculated for lead SNPs of well-powered clinical traits to identify pleiotropic loci. The CLPP thresholds were calibrated to be equivalent to p-values of associations across the six replication cohorts by applying an inverse variance-weighted meta-analysis technique. We defined the p-value of association < 0.05 as nominal evidence of replication and the p-value < 0.05/61 as a more stringent Bonferroni-corrected replication cutoff, given that 61 independent SNPs were uncovered for their pleiotropic associations to OSA in the discovery cohort  from the 1000 Genomes Project. For the fair comparison between random and predicted pleiotropic SNPs, we further filtered these random SNPs based on the OSA association p-values in 200kb windows. Overall, we kept only 11,910 random independent SNPs that have an AHI-associated SNP (p < 0.01) within 100kb distance. On all loci examined for the pleiotropy, we similarly applied the LD pruning procedure on the focal SNPs to obtain a subset of independent SNPs. During the pruning steps, SNPs with more significant JLIM p-values were preferentially retained. After the LD pruning, we obtained 995, 1,024, 967 and 961 independent SNPs for AHI, average SpO2, minimum SpO2 and event duration, respectively; of which 20, 12, 6 and 11 SNPs are predicted to be pleiotropic in the discovery cohort.To assess the sensitivity of our pleiotropy analysis, we compared the nominal replication rate of random SNPs with that of candidate pleiotropic variants identified in the discovery cohort. We started with 87,938 SNPs randomly selected across the autosomes, excluding chromosome 6 to avoid the major histocompatibility complex locus. Then, we used Plink to apply LD pruning on the random set of SNPs and obtained a subset of 28,558 independent SNPs. The LD pruning procedure ensured that the cis-expression quantitative trait loci (eQTLs) from the Gene-Tissue Expression project (GTEx release v8) [+ monocytes (n = 191), neutrophils (n = 196) and CD4+ T cells (n = 167). The RNA transcripts of BLUEPRINT samples were derived from unstimulated primary cells collected from healthy individuals of European ancestry. The genome-wide summary statistics of BLUEPRINT eQTLs were downloaded from the eQTL Catalogue [We used ease v8)  and BLUEease v8) , to examatalogue .p-value > 0.05 at all SNPs in the locus were excluded due to weak evidence of association to gene expression. Overall, a total of 5,860 clinical traits/eQTLs pairs  were tested for the pleiotropy using JLIM in the default setting . The null distribution of JLIM statistic was generated by random sampling of phenotypes from a normal distribution, sampling of genotypes from the reference LD panel , and then linearly regressing the sampled phenotypes on the genotypes. Multiallelic SNPs and rare variants with MAF < 0.05 were excluded from the analysis. The multiple testing burden was accounted for with the False Discovery Rate (FDR) by applying the Benjamini-Hochberg procedure separately to each tissue/cell type and clinical trait combination.We start with the 61 index SNPs corresponding to the candidate pleiotropic loci identified by JLIM FDR < 0.2 and cFDR consensus . Using G setting . JLIM vecis-eQTLs as clinical traits. We examined four GTEx tissues and three BLUEPRINT immune cell types, similarly to the above causal chain analysis. There are a total 57 eQTLs in the three loci satisfying: 1) the eQTL p-values < 5 x 10−8 for a SNP within the 200kb window centered at the known OSA-associated SNP, and 2) the transcription start sites being within 1 Mb from the OSA-associated SNP. Using these 57 eQTLs as clinical traits, we applied JLIM in the default setting . For the simulations of H0, the SNP in the midpoint of the genomic segment was chosen as the causative variant for well-powered traits, but no causal effect was simulated for underpowered traits. For H1, the midpoint SNP was taken as the shared causative variant for both well-powered and underpowered traits. For H2, the midpoint SNP was selected to be causal for well-powered traits, but a distinct SNP was selected as the causative variant for underpowered traits. The distinct SNP was randomly chosen in the intervals of LD relative to the midpoint SNP, selecting one in each of the following LD ranges: |r| < 0.3, 0.3 < |r| < 0.6, 0.6 < |r| < 0.8 and 0.8 < |r| < 0.95.In these loci, we generated 80,000 sets of association statistics under each of the following six scenarios , corresponding to well-powered and underpowered traits, respectively, from the following bivariate normal distribution:−5 under the assumptions of the heritability of 0.5, causal fraction of 0.01 and 1,000,000 independent markers across the genome. The parameter ρ was used to represent the correlation of causal effect sizes between two traits. For H1, we generated simulated data under no correlation (ρ = 0) as well as moderate to high correlation . For H2, we assumed ρ = 0 since the causative variants are not shared. For H0, β2 was set to 0.For each pair of causative variants, we randomly sampled the genetic effect sizes  from the following multivariate normal distribution [N1 is the GWAS sample size , m is the number of markers in the locus, D is the m x m local LD matrix, and β is the m-dimensional vector of true causal effects  of all SNPs in the locus. Here, β was set to β1 at the causative SNP and to 0 at all other SNPs. The p-values of association were calculated from the z scores as follows:zj is the association statistic at SNP j and Φ is the standard normal cumulative distribution function.JLIM only requires GWAS summary statistics for the well-powered trait. Therefore, for all SNPs ribution :z∼N. The association statistics and p-values of association were obtained by regressing the phenotypes yi on the genotypes of each SNP in the locus. We excluded multiallelic sites and variants with MAF < 0.05 from the analysis.JLIM 2.0 requires individual-level genotype data as well as association statistics for the second trait. Therefore, for the underpowered trait, we generated a discovery cohort with simulated phenotypes for all individuals; then, we calculated the association statistics by linear regression between genotypes and phenotypes, instead of sampling the summary statistics from a multivariate normal distribution. The genotype data of 10,000 individuals were obtained by subsampling from six cohorts of European Ancestry . The phenotype value 0, H1 and H2 in the genetic background of 80 loci . Because of sampling noise, in a minority of simulations, association data of some loci failed to pass the genome-wide significance threshold for well-powered traits. We rejected such simulation runs to mimic our data analysis, where we only included loci in which the clinical trait association p-value was genome-wide significant. Similarly, we excluded instances where there exists no SNP with the p-value of association < 0.01 for underpowered traits. In total, we retained 25,958, 31,858, 28,602, 29,131, 27,025 and 25,115 sets of association data for H0, H1, and H2 of distinct causative variants in low to high LD, respectively.The above process generated up to 480,000 sets of association data under scenarios of H2 simulations to follow this distribution. Specifically, we subsampled H2 datasets to fit to the following composition: 72% for |r| < 0.3, 19% for 0.3 < |r| < 0.6, 5% for 0.6 < |r| < 0.8 and 4% for 0.8 < |r| < 0.95  was set to 0.7 for H1. The proportion of H0 was assumed to be 30% of examined GWAS loci . The relative proportion of H1 and H2 was set to 1:19. The proportion of loci with two causative variants was set to 1/4 of all H1 as expected under Poisson distribution with the causal fraction of 0.01.Last, we also evaluated the accuracy of our method under the presence of multiple causative variants that were shared between two traits in H0, H1 and H2 . We evaluated the power to replicate the candidate pleiotropic loci using the subsampled GWAS datasets . The power to replicate after Bonferroni correction was estimated in the same way as other simulations. The validation cohort were assumed to be of the same genetic ancestry as the discovery cohort, and the sample sizes varied from n = 10,000 to 35,000. For pairwise analyses, we used only the main well-powered trait and underpowered trait. On the other hand, for full multi-trait analyses, we used the entire data including all ten well-powered traits and one underpowered trait.GWAS data were simulated for ten well-powered and one underpowered traits under H1 and H2 . One of ρ = 0.7). MetABF was run three times with the scale parameter of causal effect sizes set to 0.1, 0.2 and 0.4, and the Approximate Bayes Factors (ABF) were averaged over the three runs as recommended by the authors. MetABF reports the ABF relative to H0, thus we calculated the ABF of pleiotropy to the underpowered trait relative to that of no pleiotropy to the underpowered trait by summing ABFs over subsets. In contrast, we ran CPBayes in the fully automatic setting. All parameters of the pleiotropy were directly learned from the data. We used the PPAj (Posterior Probability of Association) of the underpowered trait to identify pleiotropic associations. In comparison to the two Bayesian methods, iGWAS is a non-parametric meta-analysis test. We ran iGWAS in the default setting. For all three methods, candidate pleiotropic associations were identified in the discovery cohort at the same p-value cutoff of 0.01. For the Bayesian methods, we applied the equivalent ABF and PPAj thresholds by calibrating them to have the false positive rate of 0.01 in our null simulation (H0).We benchmarked the power to replicate pleiotropic associations in underpowered cohorts by applying three meta-analysis methods–MetABF, CPBayes and iGWAS–on simulated datasets. We ran MetABF in the subset-exhaustive mode to scan all possible subsets of pleiotropic traits. The parameter for the correlation of effect sizes was set to the known value of simulation (S1 Acknowledgments(DOCX)Click here for additional data file.S1 Fig0), shared effect between well-powered and underpowered traits (H1), and distinct effects between two compared traits (H2). H1 and H2 are competing alternative hypotheses. The simulated true causal variants are indicated by red arrows.Examples of trait pairs simulated under the null Click here for additional data file.S2 Fig(A) Sensitivity to detect H1 at the same false positive rate for H0. The same p-value cutoff was used for JLIM and cFDR. For eCAVIAR, a posterior cutoff was calibrated to match the p-value cutoff using unfiltered H0 simulation data. (B) The rate of misclassifying H2 as H1. H2 loci include all loci simulating distinct causative variants in LD between 0 to 0.95. Again, the cutoffs of cFDR, eCAVIAR and JLIM were calibrated to the same specificity using H0. (C) The distribution of LD for randomly selected pairs of SNPs. This distribution has been drawn from the LD patterns between random pairs of SNPs within 80 random loci (200kb each) in the population of European ancestry. This distribution was used to simulate the LD between distinct causative variants in H2.  The rate of misclassifying H2 as H1, broken down by the LD between simulated distinct causative variants for two traits. The cutoff of JLIM p-values was set to (D) 0.05, (E) 0.01 or (F) 0.005. The dashed horizontal line indicates the JLIM p-value cutoff. θ represents the genetic resolution parameter for JLIM, set to 0.5 in this study. JLIM does not claim to distinguish H2 beyond the specified genetic resolution limit .(TIF)Click here for additional data file.S3 Fig(A) H1 and (B) H2. In all panels, each dot represents the genetic effects measured at the index SNPs. The index SNPs are defined as the lead SNPs of association to well-powered clinical traits. Occasionally, the index SNPs deviate from the causative SNPs due to sampling noise, and when this happens, we calculated the true effect size at the index SNP by multiplying the true effect of the causative SNP by the LD between the index and causative SNPs. The loci detected at JLIM p < 0.01 are indicated by cyan dots, and their density distribution are shown in contours. The loci found by cFDR (association p < 5 x 10−8 for well-powered trait and < 0.01 for underpowered trait) are represented by dots above the black horizontal line.The true and observed genetic effect sizes for underpowered traits are shown to highlight the winner’s curse in a discovery cohort. We show data from 10,000 loci each simulated under (TIF)Click here for additional data file.S4 Fig(A) The number of loci identified in a discovery cohort by pleiotropy analysis. (B-F) The number of loci replicated in a validation cohort, subdivided by the configuration of causative variants: (B) H1, H2 with the LD between causative variants to be in the ranges of (C) |r| < 0.3, (D) 0.3 < |r| < 0.6, (E) 0.6 < |r| < 0.8 and (F) 0.8 < |r| < 0.95. In all panels, simulation was conducted under the following parameters: A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in simulated discovery cohorts , and then the candidate pleiotropic loci were tested for replication in simulated validation cohorts of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR), eCAVIAR, Joint Likelihood Mapping (JLIM), or the intersection of eCAVIAR or JLIM and cFDR, all at the p-value cutoff of 0.01 . The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated (ρ = 0.7) under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.(TIF)Click here for additional data file.S5 Figp-value cutoff of 0.01. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of (A) 1:19, (B) 1:9, (C) 1:4, and (D) 1:0. The effect sizes of causative variants are correlated (ρ = 0.7) under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR), Joint Likelihood Mapping (JLIM), or the intersection of both, all at the (TIF)Click here for additional data file.S6 Figp-value cutoff of 0.01. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was varied to (A) 0%, (B) 10%, (C) 20% and (D) 30%, and the remaining loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated (ρ = 0.7) under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR), Joint Likelihood Mapping (JLIM), or the intersection of both, all at the (TIF)Click here for additional data file.S7 Fig(A) The consensus method (red line) selected 12.9 candidate pleiotropic loci by taking the intersection between JLIM p < 0.01 and cFDR (association p < 0.01 for an underpowered trait). For the comparison, we tightened cFDR threshold to underpowered trait assoc p < 0.0012 so to identify the same number of candidate loci in a discovery cohort (black line). (B) Similarly, the consensus method (red line) found 7.5 candidate loci by taking the intersection between JLIM p < 0.005 and cFDR (assoc p < 0.005 for underpowered trait). The cFDR threshold was tightened to underpowered trait assoc p < 0.00060 for the same number of candidates (black line). In both panels, the 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated (ρ = 0.7) under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR) or by the JLIM/cFDR consensus method. (TIF)Click here for additional data file.S8 Fig(A) The consensus method (red line), by taking the intersection between JLIM p < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. To match this false positive rate, we tightened cFDR threshold to p < 0.0038 (black line). (B) Similarly, the consensus method (red line) showed the empirical false positive rate of 0.0018 in H0 when the intersection was taken at JLIM and cFDR p < 0.005. To match the false positive rate, the cFDR threshold was tightened to p < 0.0018 (black line). The cFDR p-value refers to the p-value of association to an underpowered trait. In both panels, the 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated (ρ = 0.7) under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR) or by the JLIM/cFDR consensus method. (TIF)Click here for additional data file.S9 Fig(A) 500, (B) 1,000, (C) 2,500 and (D) 5,000 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR) or by the JLIM/cFDR consensus method. The consensus method (red line), by taking the intersection between JLIM p < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. To match this false positive rate, we tightened cFDR threshold to p < 0.0038 (black line). The cFDR p-value refers to the p-value of association to an underpowered trait. The GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated (ρ = 0.7) under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of (TIF)Click here for additional data file.S10 Figp < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. To match this false positive rate, we tightened cFDR threshold to p < 0.0038 (black line). The cFDR p-value refers to the p-value of association to an underpowered trait. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated with (A) ρ = 0.0, (B) ρ = 0.5, (C) ρ = 0.7 and (D) ρ = 0.9 under H1 but uncorrelated under H2. In all panels, Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR) or by the JLIM/cFDR consensus method. The consensus method (red line), by taking the intersection between JLIM (TIF)Click here for additional data file.S11 Fig(A) the same (CEU) and (B) different (YRI) genetic ancestry . The LD patterns of genetic ancestry were obtained from the 1000 Genomes Project data. In both panels, the candidate loci were identified by conditional false discovery rate (cFDR) or by the JLIM/cFDR consensus method. The consensus method (red line), by taking the intersection between JLIM p < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. To match this false positive rate, we tightened cFDR threshold to p < 0.0038 (black line). The cFDR p-value refers to the p-value of association to an underpowered trait. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. The effect sizes of causative variants are correlated with ρ = 0.7 under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of (TIF)Click here for additional data file.S12 Figp < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. To match this false positive rate, we tightened cFDR threshold to p < 0.0038 (black line). The cFDR p-value refers to the p-value of association to an underpowered trait. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 was set to 30%, and the remaining 70% of loci were split to H1 and H2 at the ratio of 1:19. In Panel (A), only one causative variant was simulated for H1, whereas in Panel (B), up to two causative variants were simulated for H1. The proportion of loci with two causative variants was set to 1/4 of all H1 as expected under Poisson distribution with the causal fraction of 0.01. The effect sizes of causative variants are correlated with ρ = 0.7 under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs.A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by conditional false discovery rate (cFDR) or by the JLIM/cFDR consensus method. The consensus method (red line), by taking the intersection between JLIM (TIF)Click here for additional data file.S13 Figp < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. Bayesian posterior thresholds for MetABF (green) and CPBayes (purple) calibrated using H0 loci to match the false positive rate of the consensus method. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 varied to (A) 0%, (B) 10%, (C) 20% and (D) 30%, and the remaining loci were split to H1 and H2 at the ratio of 1:19. In all panels, the effect sizes of causative variants are correlated with ρ = 0.7 under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs. In (E), the fraction of H1 among replicated loci is compared among three methods when the proportion of H0 is 30% (similar for different proportions of H0).A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the same genetic ancestry . The candidate loci were identified by the JLIM/cFDR consensus method, MetABF and CPBayes. The consensus method (red line), by taking the intersection between JLIM (TIF)Click here for additional data file.S14 Figp < 0.01 and cFDR p < 0.01, showed the empirical false positive rate of 0.0038 in simulated H0 dataset. Bayesian posterior thresholds for MetABF (green) and CPBayes (purple) calibrated using H0 loci to match the false positive rate of the consensus method. The 2,500 GWAS peaks consist of the loci simulating no causal effect for underpowered traits (H0) and those simulating the same causal effect between two traits (H1) or distinct causal effects (H2). The proportion of H0 varied to (A) 0%, (B) 10%, (C) 20% and (D) 30%, and the remaining loci were split to H1 and H2 at the ratio of 1:19. In all panels, the effect sizes of causative variants are correlated with ρ = 0.7 under H1 but uncorrelated under H2. Bonferroni correction was applied on replication tests. The shaded area denotes the 95% CIs. In (E), the fraction of H1 among replicated loci is compared among three methods when the proportion of H0 is 30% (similar for different proportions of H0).A total of 2,500 association peaks from well-powered GWAS studies  were tested for pleiotropy in a discovery cohort , and then the candidate pleiotropic loci were tested for replication in an independent validation cohort of the different genetic ancestry . The candidate loci were identified by the JLIM/cFDR consensus method, MetABF and CPBayes. The consensus method (red line), by taking the intersection between JLIM (TIF)Click here for additional data file.S15 Fig0, H1 and H2. One of the well-powered traits is ascertained to have a genome-wide significant association peak, and the rest of well-powered traits were randomly decided to have the same or distinct causative variants by sampling from a binomial distribution Binom. The underpowered trait harbors no causative variant, the same causative variant or distinct causative variant depending on whether it is simulating H0, H1 or H2. Effect sizes of all traits sharing the same causative variant were sampled together from a multivariate normal distribution with the correlation parameter of 0.7. Effect sizes of traits simulating distinct causative variants were sampled independently. GWAS association statistics at the focal SNP were generated with the sample sizes of n = 150,000 for the well-powered traits and n = 10,000 for the underpowered trait. The validation cohort of the same ancestry was simulated with the sample sizes of n = 10,000 to 35,000. From these simulated H0, H1 and H2 loci, a total of 2,500 GWAS loci were randomly selected at the proportions of 30%, 3.5% and 66.5%, respectively (H1:H2 ratio of 1:19). (B) We applied iGWAS, CPBayes and MetABF on these data. Dashed lines indicate pairwise analyses for which only one genome-wide significant well-powered trait was compared with an underpowered trait. Solid lines indicate multi-trait analyses using the full set of ten well-powered traits and one underpowered trait. Candidate pleiotropic loci were selected for replication analysis at the cutoffs of p-value of 0.01 (iGWAS), or equivalent posterior probability thresholds calibrated in the H0 dataset (CPBayes and MetABF). Bonferroni correction was applied on replication tests in the validation cohort.Ten well-powered traits and one underpowered trait were simulated under H(TIF)Click here for additional data file.S16 FigThe plot shows the fractions of randomly selected and putative pleiotropic loci with OSA associations that are nominally replicated in the meta-analyzed independent validation cohort .(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Eubacterium rectale in decreasing plasma levels of hydrogen sulfite—a toxin that affects cardiovascular function. Lastly, based on analysis of the plasma metabolome of 311 individuals at two time points separated by 4 years, we observed a positive correlation between the stability of metabolite levels and the amount of variance in the levels of that metabolite that could be explained in our analysis. Altogether, characterization of factors that explain inter-individual variation in the plasma metabolome can help design approaches for modulating diet or the gut microbiome to shape a healthy metabolome.The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual’s genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics, respectively. Moreover, a diet quality score derived from metabolite levels was significantly associated with diet quality, as assessed by a detailed food frequency questionnaire. Through Mendelian randomization and mediation analyses, we revealed putative causal relationships between diet, the gut microbiome and metabolites. For example, Mendelian randomization analyses support a potential causal effect of  The influence of an individual’s genetics, diet and gut microbiome on their plasma metabolome was studied in 1,368 individuals and Mendelian randomization and mediation analyses were used to unveil causal relationships between diet, the gut microbiome and the metabolome. Elucidating the genetic, dietary and microbial factors that shape human metabolism is crucial for understanding the origin and determinants of plasma metabolites, and hence for the eventual design of intervention strategies aimed at a healthy metabolome.The plasma metabolome represents a functional readout of metabolic activities within different organs and tissues of the body. Levels of specific plasma metabolites may therefore reflect the presence of specific diseases or an individual’s susceptibility to developing complex metabolic diseases such as cardiovascular and kidney disorders, diabetes, cancers and Crohn’s disease4. For instance, a reference map of potential determinants of the human serum metabolome was established in 491 individuals from an Israeli cohort, and the authors reported 335 metabolites that were significantly explained by diet and 182 that were explained by the gut microbiome3. More recently, the Personalized Responses to Dietary Composition Trial assessed the impact of diet and the microbiome on host metabolism in 1,098 individuals from the United Kingdom and United States and observed that the microbial species associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic and postprandial markers4.Inter-individual variations in the human plasma metabolome have already been linked to genetics, diet and the gut microbiome in several cohort-based studies5 and Genome of the Netherlands (GoNL)6 cohorts, including LLD1 , LLD2 (n = 237) and GoNL (n = 77). In addition, 311 LLD1 individuals were followed up after 4 years7. For each participant, we had information on the gut microbiome, genetic background and dietary habits. In addition, we assessed whether diet-associated metabolites can be used to predict an individual’s dietary quality score reflecting diet‒disease relationships8 and examined whether genetics-associated metabolites can pinpoint dysregulated molecular pathways in complex diseases. Importantly, as potential causal relationships among metabolites, diet and the microbiome remain largely unexplored, metabolites associated with multiple factors offered us an opportunity to infer their underlying causality using Mendelian randomization (MR) and mediation analyses9.As diet, genetics and the gut microbiome are highly heterogeneous between different countries, we aimed to: (1) systematically identify dietary, genetic and microbial factors that are associated with plasma metabolites; (2) identify which of the three factors  explains the most inter-individual variability in metabolites compared with the other two (the dominant factor); and (3) assess their causal relationships using in silico approaches. To do so, we quantified the plasma levels of 1,183 metabolites in 1,368 individuals from the population-based Lifelines DEEP (LLD)5 sub-cohorts (LLD1 and LLD2) and the GoNL6 cohort . We also included two independent replication cohorts: 237 LLD2 participants for whom we had genetic and dietary data and 77 GoNL participants for whom only genetic data were available  correlations among the 1,183 metabolites  method . Furthermore, out of the 38 genetics-dominant metabolites, ten were lipid species and eight were amino acids. Taken together, our analysis highlights that one factor—either dietary, genetic or microbial—can have a dominant effect over the other two in explaining the variances of plasma metabolites, with diet or the microbiome being particularly dominant. However, we also found that the variances in 185 metabolites were significantly attributable to more than one factor  and assigned the dominant factor for each metabolite if one factor explained more variance than the other two. Inter-individual variations in 610 metabolites were mostly explained by diet, 85 were explained by the gut microbiome and 38 were explained by genetics  , and its genetic association was not replicated at the second time point.Temporal changes in plasma metabolites can reflect changes in an individual’s diet, gut microbiome and health status. When assessing the plasma metabolome in the 311 LLDHaving established the variances in metabolites explained by diet, genetics and the gut microbiome and the dominant factors that explained most of this variance, we focused on detailing specific associations and on the potential implications of our findings for assessing diet quality and improving our understanding of the genetic risk of complex diseases and the interaction and causality relationships among diet, the microbiome, genetics and metabolism.Spearman < 0.05) between 74 dietary factors and 726 metabolites  and fish intake  as well as a lower level of 1-methylhistidine in vegetarians 21, Protein Score22 and Modified Mediterranean Diet Score23) and found that the HEI predicted by plasma metabolites was also significantly associated with the FFQ-based HEI  data that reflected the relative diet quality based on diet‒disease relationships1 identified 48 study-wide, independent genetic associations between 44 metabolites and 40 single-nucleotide polymorphisms (SNPs) ; Fig. 1 follow-up or the two independent replication datasets  gene  with a SNP, rs35246381, that was recently reported to be associated with urinary AFMU26. AFMU is a direct product of NAT2 activity and has been associated with bladder cancer risk27. Interestingly, the plasma level of AFMU was associated not only with coffee intake  and SNP rs1495741 near the SLCO1B1, FADS2, KLKB1 and PYROXD2 (Supplementary Table r2 = 0.72 in Northern Europeans from Utah) in SLCO1B1, which encodes the solute carrier organic anion transporter family member 1B1. Expression of the SLCO1B1 protein is specific to the liver, where this transporter is involved in the transport of various endogenous compounds and drugs, including statins28, from blood into the liver. The SLCO1B1 locus has also been linked to plasma levels of fatty acids and to statin-induced myopathy29. Furthermore, we detected a genetics–microbiome interaction between rs67981690 and microbial fatty acid oxidation pathways in regulating plasma levels of 5′-carboxy-γ-chromanol (P = 1.5 × 10−3), where the association of the bacterial fatty acid oxidation pathway with plasma levels of 5′-carboxy-γ-chromanol was dependent on the genotype of rs67981690 . The first two novel SNPs—rs13100173 at HYAL3 and rs11741352 at ARSB—were associated with N-acetylgalactosamine-4-sulfate , suggesting that mQTLs can also pinpoint potential metabolite–protein interactions. The third novel mQTL was rs17789626 at SCLT1, which was associated with mizoribine—a compound used to treat nephrotic syndrome31.To identify novel mQTLs, we performed a systematic search of all published mQTL studies from 2008 onwards  and 117 associations with five phytoestrogens  (FDRP < 1 × 10−5 and with 45 metabolites (Supplementary Table P = 2 × 10−3 obtained from the inverse variance weighted (IVW) test)35, we observed four potential causal relationships at baseline that could also be found in the follow-up in the microbiomes to metabolites direction  and abundant (mean abundance = 8.5%) in both our cohorts and in other populations41. As a strict anaerobe, E. rectale promotes the host’s intestinal health by producing butyrate and other short-chain fatty acids from non-digestible fibers42, and a reduced abundance of this species has been observed in subjects with inflammatory bowel disease43 and colorectal cancer44 compared with healthy controls. As a toxin, hydrogen sulfite interferes with the nervous system, cardiovascular functions, inflammatory processes and the gastrointestinal and renal system45. Our results thus reveal a potential new beneficial effect of E. rectale.We further found that increased abundance of microbial adenosylcobalamin biosynthesis (coenzyme B12) was associated with reduced plasma levels of 5-hydroxytryptophol Fig. —a uremicale Fig. —a core g46 and microbial genomic structural variants (SVs)47. These two tools profile microbial genomic entities that are implicated in metabolic functions. By associating 1,183 metabolites with 3,075 gutSMASH pathways and 6,044 SVs  and 4,262 deletion SVs (dSVs); see LLD1 < 0.05; PLLD1 follow-up < 0.05; Supplementary Tables Olsenella and Clostridium species) and the hydroxybenzoate-to-phenol pathway (in Clostridium species) responsible for hippuric acid and phenol sulfate biosynthesis, were associated with the hippuric acid  and phenol sulfate mediation < 0.05 and Pinverse-mediation > 0.05; Fig. Next, we carried out a mediation analysis to investigate the links between diet, the microbiome and metabolites. For 675 microbial features that were associated with both dietary habits and metabolites (FDR < 0.05), we applied bi-directional mediation analysis to evaluate the effects of microbiome and metabolites for diet  complex  that encodes an ATPase responsible for transmembrane transport of various substrates51. This Ruminococcus species vSV mediated the effect of fruit consumption on plasma levels of urolithin B  for whom we also have extensive dietary records, genetics and gut microbiome data, we carried out systematic diet, genetics and microbiome association analyses. Our results show that diet and the gut microbiome play a more dominant role than genetics in explaining inter-individual variability in metabolism, and the more variance that was explained in a metabolite, the more stable that metabolite was over time.3, highlighting that the plasma metabolome mirrors personal dietary habits. Nevertheless, it remained to be established whether it was possible to assess an individual’s diet quality score based on their plasma metabolome. Using a machine learning-based prediction model, we showed that diet quality estimated by an individual’s plasma metabolome showed a significant correlation with diet quality estimated by the FFQ, suggesting that the plasma metabolome to some extent reflects diet quality.Dietary components are fundamental resources for the plasma metabolome, and a recent study illustrated that an individual’s dietary habits can predict the levels of specific metabolites present in plasma53, these investigations did not capture the specific microbial enzymes responsible for metabolite generation, even though this information is required to connect associated links to underlying molecular mechanisms54. Using gutSMASH and microbial SVs, we identified putative metabolic functionalities for previously unannotated microbial genetic sequences. In addition, through bi-directional mediation analysis, we identified hundreds of mediation linkages that provide insight into diet‒microbiome interactions in human metabolic health, as illustrated by several metabolites  that have previously been related to cardiometabolic and kidney diseases32. Notably, these mediation linkages mainly show that the impact of diet composition on the microbiome can be mediated by metabolites, highlighting the pronounced selective power of dietary habits in shaping the gut microbiome. Nevertheless, as these results are mainly based on observational data, interpretation of such associations should be made with caution, and future intervention and experimental studies that focus on specific diet and microbial genomic capacities are essential to confirm causality.Dietary components serve as substrates in gut microbial metabolic pathways, leading to the formation of a series of metabolites that can be absorbed from the intestine into the host’s circulation. Although earlier studies had linked gut microbial taxonomic abundances to plasma metabolites32. The causal relationships between microbiomes and metabolites that we have established thus reveal potential metabolic functionalities of gut microbes that impact on human health.Apart from diet and the gut microbiome, human genetics also acts as a potential determinant of the plasma metabolome. With this metabolome dataset, we not only replicated previously reported mQTLs, but we also identified three mQTLs involving three loci not previously known to be associated with any metabolites. The mQTLs we characterized could be linked to cardiometabolic and chronic kidney diseases, as illustrated by the tissue-specific gene expression analysis and pleotropic mQTL effects. We also used genetic variants as instruments in MR to infer causal relationships between the gut microbiome and metabolites. This analysis showed that the microbiome may causally contribute to the levels of toxins (hydrogen sulfite and 5-hydroxytryptophol) related to chronic kidney disease and cardiometabolic diseases2 and GoNL cohorts. Nonetheless, our study was still underpowered. At the observed effect size in the discovery set and at the P < 0.05 level, we have 80% power to replicate the findings for 100% of the genetic associations, but only 60% power for the microbial associations. In addition, causal relationships between the microbiome and metabolites were based on one-sample MR; replication in independent cohorts with larger sample sizes and two-sample MR analysis may further strengthen those observations and better establish possible biological significance. Finally, the LLD cohort was comprised of Dutch participants of Caucasian ethnicity from the northern region of the Netherlands. It is thus possible that the LLD results are biased towards a region-specific microbial background constrained by host genetics and local environmental exposures. We primarily focused on biologically plausible mechanisms by integrating different layers of omics to provide mechanistic hypotheses, and experimental validation is thus warranted.We acknowledge several limitations in our study. Untargeted plasma metabolome was profiled using FI-MS without compound separation using liquid chromatography columns, and no genuine standards were used. Although the abundances of a few metabolites were well validated using the LC-MS/MS or NMR platforms, identification and quantification of mass peaks using the FI-MS approach is still generally less accurate than in the classical LC-MS/MS platform. We systematically investigated the contributions of genetics, diet and the microbiome to inter-individual metabolome variations and replicated the explained variance in two independent metabolic profile assessments of the same cohort performed 4 years apart. However, overfitting may still have been an issue and could potentially have biased the conclusions. Our findings should be further replicated using an independent cohort for which similarly extensive datasets on the metabolome, genetics, the microbiome, diet, disease and medication are also available. However, it is challenging to obtain such cohorts. We included as many participants as possible for the replication, including two independent sets of individuals from the LLDTaken together, the dietary, genetic and microbial associations with plasma metabolites and the causal and mediation linkages that we report here provide a comprehensive resource that can guide follow-up studies aimed at designing preventive and therapeutic strategies for human metabolic health.n = 1,500) is a sub-cohort of the large prospective Lifelines cohort study from the north of the Netherlands55. The cohort is 58% female and 42% male, with a mean age of 45.04 years (s.d. = 13.60). The mean BMI is 25.26 (s.d. = 4.18) and 12% of participants are obese (BMI > 30)5. All Lifelines participants signed an informed consent form before sample collection. The ethics review board of the University Medical Center Groningen has approved the study with reference number M12.113965. For this study, we involved 1,054 out of 1,500 LLD participants (LLD1) for whom detailed dietary habit, stool microbiome and plasma untargeted metabolomics information is available. After removing relatives and genetic outliers, 927 individuals were subjected to genetics analysis, and for 311 of them we also have 4-year follow-up metabolome data (Supplementary Table n = 77)56 and the remaining set of LLD participants for whom we have genetics and plasma metabolome data but not microbiome data  phosphazene and 80 nM taurocholic acid for online mass calibration. Then, 100 µl of samples were injected into the ionization source in a random order. Data were acquired in profile mode, centroided and analyzed using MATLAB (MathWorks). Missing values were filled by recursion in the raw data. Upon identification of consensus centroids across all samples, ions were putatively annotated by accurate mass and isotopic patterns. Based on the HMDB (version 4.0)15, a list of the expected ions found under these conditions was generated, including deprotonated, fluorinated and all major adducts. As this method does not employ chromatographic separation or in-depth MS2 characterization, it is not possible to distinguish between compounds with identical molecular formulas. The confidence of annotation reflects level 4 but is higher in practice in the case of intermediates of primary metabolism because they are the most abundant metabolites in cells11. Ion intensities were normalized by quantile normalization to compensate for slight variations in the sample amount. In this way, 1,183 peaks were annotated based on accurate mass using a 1-mDa tolerance  with age, sex, BMI, smoking, 78 dietary habits, 39 diseases and the use of 44 medications  within 15 min of production. Subsequently, a nurse visited the participant to pick up the fecal samples on dry ice and transfer them to the laboratory. Aliquots were then made and stored at −80 °C until further processing . The same protocol for fecal DNA isolation and metagenomics sequencing was used in all four cohorts. Fecal DNA isolation was performed using the AllPrep DNA/RNA Mini Kit . After DNA extraction, fecal DNA was sent to the Broad Institute of MIT and Harvard, where library preparation and whole-genome shotgun sequencing were performed on an Illumina HiSeq platform. From the raw metagenomics sequencing data, low-quality reads were discarded by the sequencing facility and reads belonging to the human genome were removed by mapping the data to the human reference genome (version NCBI37) using KneadData (version 0.4.6.1) and Bowtie 2 (version 2.1.0)59. Microbial general pathways were determined using HUMAnN2 (ref. 60), which maps DNA/RNA reads to a customized database of functionally annotated pan-genomes. HUMAnN2 reported the abundances of gene families from the UniProt Reference Clusters61 (UniRef90), which were further mapped to microbial pathways from the MetaCyc metabolic pathway database63. In total, we detected 156 species and 343 pathways that were present in at least 10% of samples, retaining 98% of the original species composition and 100% of the original functional composition. The relative abundances of both species and pathway datasets were centered-log-ratio transformed, followed by inverse-rank transformation, before subsequent analysis64.Microbial taxonomic profiles were generated using MetaPhlAn2 (version 2.7.2)47 to classify SVs that were either completely absent from the microbial genome of some samples (dSVs) or whose coverage was highly variable across samples (vSVs). Before SV classification, we applied an iterative coverage-based read assignment algorithm that resolves ambiguous read assignments to regions that are similar between different bacteria, using information on bacterial relative abundances in the microbiome, their genomic sequencing coverage and sequencing and alignment qualities47. In total, we classified 4,262 dSVs and 1,782 vSVs from 41 microbial species that were present in at least 10% of samples. The vSV data were inverse-rank transformed for subsequent analysis.We applied the SGV-Finder pipeline46. In total, we generated 3,075 microbial strain-level metabolite-specific pathways that were present in at least 10% of samples. The abundance of these pathways was recorded as reads per kilobase of transcript per million reads mapped, and inverse-rank transformation was applied before subsequent analysis.Metabolite-specific pathways were generated using the gutSMASH algorithm65. Quality control checks on the LLD cohort were performed using the Haplotype Reference Consortium (version 1.0) preparation checking tool (version 4.2.3). We then uploaded the resulting VCF files to the Michigan Imputation Server66. Phasing and imputation were performed using the option SHAPEIT for phasing, population EUR and the mode Quality Control and Imputation. For all steps, we used version R1 as a reference67. We further excluded SNPs that had an imputation quality r2 < 0.5, failed the Hardy–Weinberg equilibrium test (P < 1 × 10−6), had a call rate of <95% or had a minor allele frequency of <5%. In total, we obtained genotype data for 5.3 million SNPs (genome build hg19) for 927 individuals after removing relatives and outliers. The genotypes of the GoNL samples were obtained by whole-genome sequencing.Microarray genotype data for the LLD cohort were generated using the CytoSNP-12 Beadchip and Immunochip assays, as previously describedr2). Features were assigned to different clusters based on 70% dissimilarity and the central feature in each cluster was selected as representative. All representative features were further included in PERMANOVA to estimate the combined contribution to inter-individual metabolome variation.We applied feature selection based on the permutational multivariate analysis of variance using distance matrices (PERMANOVA) procedure to estimate the contributions of different factors to inter-individual variations of the whole plasma metabolome. Phenotypic, genetic and microbial contributions were estimated based on the 927 participants for whom plasma metabolites, a stool microbiome, phenotypic data and genotype were available. First, we used each raw phenotypic and microbial feature to estimate inter-individual metabolic variations using the adonis function from vegan (version 2.5.5) with 1,000 times permutation. Only phenotypic and microbial features that could estimate inter-individual metabolic variations at a permutational FDR of <0.05 were kept. For genetic variants, we used SNPs with significant mQTLs. To deal with the colinearity of selected features, we applied hierarchical clustering analysis based on a feature inter-correlation distance matrix  above this threshold were reported. We also tested the impact of physical activities on QTL effect by re-running the QTL analysis in 855 Lifelines participants for whom we had SQUASH  physical activity scores24.This analysis involved the 927 participants for whom there were genotype, plasma metabolome and phenotypic data. After adjusting for the first two genetic principal components, age, sex, BMI, smoking, 78 dietary habits, 40 diseases and 44 medications, as described above, a Java-based pipeline (version 1.4nZ)71. We therefore adjusted all microbial datasets for these confounding factors, together with age, sex and smoking. For microbial changes 4 years apart, we regressed out age and sex. Next, Spearman correlation was applied to check the associations between metabolites and microbial features, and P values were adjusted using the Benjamini–Hochberg procedure.We previously reported that several medications can alter the gut microbiome significantly, including proton pump inhibitors, antibiotics and laxativesy = a + b + a × b). P values were adjusted using the Benjamini–Hochberg method.For metabolites associated with at least two types of factor , we further performed interaction analysis by assessing pairwise interactions between the two factors using a linear model  made it an attractive methodology for our analysis. We believe that, while the overall variance explained might be an underestimation of the predictive power of the available data layers, the relative variability explained by each data layer should be representative of the dominant factor that explains most variance in each metabolite.To estimate the variance of each metabolite that was contributed by dietary, genetic and microbial features, we applied machine learning-based lasso regression from the glmnet package (version 2.0.16). While ensemble machine learning methods have previously been shown to outperform the predictive capabilities of linear methods such as lassor2 and F-test P value were recorded. The FDR was calculated based on the Benjamini–Hochberg procedure. We also applied Elastic Net from the glmnet package (version 2.0.16) to estimate the variance of each metabolite contributed by different factors and compared the results with the above method.All of the dietary, microbial  and genetic features that were significantly associated with a specific metabolite at FDR < 0.05 were involved in the model. These features were further selected using lasso with a lambda that gave a minimum mean error from a tenfold cross-validation in order to control for overfitting and to provide a conservative estimate of model performance. Finally, features selected by lasso were included in the linear model to estimate the variance contributed by different factors, and the adjusted The levels of all plasma metabolites were included in the principal component analysis. We applied the vegdist function from the R package (version 2.5.5) vegan to calculate the Euclidean dissimilarity matrix based on the metabolite levels. Subsequently, classical metric multidimensional scaling was carried out based on the Euclidean distance matrix to obtain different principal coordinates.We used the Spearman correlation to check how consistent the levels of individual metabolites were between the baseline and 4-year follow-up. Metabolites with larger correlation coefficients were assumed to be more stable.8 associations with each metabolite in 1,054 LLD samples and selected the significant metabolite features (P < 0.05) for lasso regression, as described above. Metabolites selected by lasso were used to build the linear model, and 230 LLD2 samples with both diet score and plasma metabolome information available were used as validation. Adjusted r2 and the P value from the F-test were reported to reflect the performance of the prediction model. We also carried out these analyses for four other dietary scores: the alternate Mediterranean Diet Score20, HEI21, Protein Score22 and Modified Mediterranean Diet Score23.We first checked the Lifelines Diet Score25.Summary statistics of independent mQTLs were used for tissue-specific gene expression analysis with FUMAP < 1 × 10−5 in the baseline that could also be found in the follow-up samples with the same direction of association (P < 0.05) and on the 45 metabolites with significant associations with the microbiome (FDRLLD1 < 0.05 and PLLD1 follow-up < 0.05). The relaxed significance threshold for choosing microbiome-associated SNPs as genetic instruments was used to increase the number of SNPs available for analyses, as described previously9.To evaluate whether the microbiome can causally contribute to plasma metabolites, we applied bi-directional MR analyses. A microbiome GWAS was performed using the same approach we applied for the metabolite GWAS, after correcting for age, sex, smoking and the use of proton pump inhibitors, antibiotics and laxatives. We focused on the 37 microbial features associated with at least three independent genetic variants at 35 and weighted median72. Therefore, MR estimates were calculated using Wald ratios and these Wald ratios were meta-analyzed using the IVW method35. In addition, we report MR estimates calculated using the weighted median test. We kept only the results based on three or more SNPs. To ensure the validity of the results, several sensitivity analyses were performed. We excluded MR estimates potentially driven by horizontal pleiotropy (removing results with MR-Egger36 intercept P < 0.05) and heterogeneity (removing results with Cohran’s Q test35P < 0.05). In addition, we carried out leave-one-out analysis73 to check whether the MR estimates were possibly driven by a single SNP . Multiple testing correction was performed using the Benjamini–Hochberg approach based on IVW P values. To avoid complex causality relationships, we excluded the results that showed a nominally significant MR estimate in the other direction (P < 0.05). For this analysis, metabolite-associated SNPs at a P value cut-off of 1 × 10−5 in the baseline group and P < 0.1 in the follow-up group were used as genetic instruments in IVW-based MR.MR analysis was done using the TwoSampleMR (version 0.5.5) R package. While this package was developed for two-sample MR analysis, a recent paper showed that it is possible to use most two-sample MR methods in the one-sample setting, including IVWLLD1 < 0.05 and PLLD1 follow-up < 0.05) and dietary habits (FDR < 0.05), we first checked whether the dietary habits were associated with the metabolite using Spearman correlation (FDR < 0.05). Next, we carried out bi-directional mediation analysis with interactions  between mediator and outcome taken into account using the mediate function from mediation (version 4.5.0) to infer the mediation effect of metabolites and microbiome for dietary impacts74. The FDR was calculated based on the Benjamini–Hochberg procedure.For microbial features associated with both metabolites (FDRFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-022-02014-8.Supplementary InformationSupplementary Figs. 1–5.Reporting SummarySupplementary TablesSupplementary Tables 1–22.


PLEC, PLEKHA5, TONSL, PTGER4, and LCORL genes. In addition, some important candidate genes, like GPAT3, CEBPB, AGO2, SLC37A1, and FNDC3B, were found to participate in fat metabolism or mammary gland development. These results can be used as candidate genes for milk production, somatic cell score, and body conformation traits of Holstein cows, and are helpful for further gene function analysis to improve milk production and quality.Milk production and body conformation traits are critical economic traits for dairy cows. To understand the basic genetic structure for those traits, a genome wide association study was performed on milk yield, milk fat yield, milk fat percentage, milk protein yield, milk protein percentage, somatic cell score, body form composite index, daily capacity composite index, feed, and leg conformation traits, based on the Illumina Bovine HD100k BeadChip. A total of 57, 12 and 26 SNPs were found to be related to the milk production, somatic cell score and body conformation traits in the Holstein cattle. Genes with pleiotropic effect were also found in this study. Seven significant SNPs were associated with multi-traits and were located on the  Milk is a source of nutrients essential for human growth and development. The milk production traits are important for the dairy industry. Body conformation traits have been applied in several countries with the development of dairy cattle breeding since they are closely related to the health , productDGAT1 and SCD1 to be highly associated with the composition of milk-fat (long-chain fatty acid). Kiser et al. . A study by Schennink et al.  has rever et al.  verifiedn cattle . Bouwmann cattle  and Vanvn cattle  have repThis study conducted GWAS using the Illumina Bovine HD100k(100k) BeadChip, for identifying important candidate genes or variants related to milk production, somatic cell score, and body conformation traits. There was an expectation for discovering novel genetic variations or candidate genes.This experiment involved 1,313 cows from 7 different pastures in Heilongjiang Province. The use and care of the animals in this study were approved by the Animal Care Advisory Committee, Northeast Agricultural University , and all the experimental procedures were according to the university's guidelines for animal research.The samples were collected from the tail roots near the hips of the cows. The DNA in the hair was extracted and genotyped using Illumina Bovine HD100k BeadChip, containing 95,256 SNPs. The markers with minor allele frequencies <0.05 and call rates <0.90 were filtered out and individuals with a call rate of 0.80 or greater were selected. These SNPs were distributed across 29 chromosomes.The SNP genotypes of these individuals were used to estimate the population stratification based on principal component analysis (PCA), and Plink (version 1.9)  was used−1 matrix must be replaced by H−1  of all animal milk production traits, somatic cell score, and body conformation traits, using single-step genomic best linear unbiased prediction (ssGBLUP). The ssGBLUP was developed to integrate all the information including genotypes, phenotypes, and pedigree information in one step, and each SNP effect was calculated using the FarmCPU method  based ond by H−1 . The speWhere y was each phenotypic value vector; b is the fixed effect of the field and the PCA effect to explain the population stratification, and u is a vector of animal effects. The e was a vector of random residual effects with e~N, and X, Z were incidence matrices for b and u, respectively.22 is a numerator relationship matrix for genotyped animals, and G is a genomic relationship matrix  referred to as the ARS-UCD1.2 assembly. The square of the correlation coefficient (r2) between the two loci is used to evaluate the range of LD measurement, because r2 is considered to be more robust and not affected by changes in allele frequency and population size (2) between all SNP pairs in the cow population to estimate the LD of the whole genome, and the LD decay map with distance of the cow population was visualized.The cattle QTL data were downloaded from the Cattle QTL database , BovineHD1400000206 (BTA 14:0.49 Mb), BovineHD1400000287 (BTA 14:0.88 Mb), and BovineHD1400011649 (BTA 14:38.57 Mb) see . The bovP = 5.84E-13) was located downstream of the PTGER4 gene.This study detected 10, 7, and 11 significant SNPs related to BFCI, DCCI, and FTLEG, respectively. There were 4 SNPs distributed on BTA 16. Three SNPs were found to be possibly as pleiotropism SNPs, including BovineHD4100004660 (BTA 6:38.22 Mb), BovineHD1300012605 (BTA 13:42.81 Mb) and BTA-50244-no-rs (BTA 20:34.30 Mb), respectively. Of these significant SNPs, the BTA-50244-no-rs SNP related to BFCI  and ARS-BFGL-NGS-55227 (50.8 Mb), respectively. Of these, BovineHD1400000206 associated with fat yield . But the nearest gene on this significantly SNP is the TONSL gene (1.65 kb), which is a neighboring gene to DGAT1. So, the DGAT1 gene was not detected in this study. The study by Ning et al.   related to the body conformation traits. This study did not detect the above genes to be associated with body conformation traits.In this study, FarmCPU was applied for screening the QTLs related to the milk production traits, health traits, and body conformation traits. A total of 95 significant SNPs were detected, located on the 93 candidate genes. Of these genes,  studies , 24, 39. to milk  also obtpulation . Cole etpulation  identifip width) . The 770p width)  to identThese inconsistencies might be due to differences in the detection platforms or algorithms used in the corresponding analysis, changes in the genetic background of the analyzed cattle, differences in the size and structure of the study population, or random or technical errors in some analyses. This also indicated that there are many important genetic markers or candidate genes in the bovine genome that are yet to be discovered.DGAT1 gene is related to milk yield  can interlink different elements of the cytoskeleton. The PLEC gene was found to be associated with multiple traits, like MY, FP, and PP. Dan Wang et al. , associated with the fat percentage of milk  and fertility  related to economically important traits in dairy cows have been identified, due to insufficient sample size and insufficient marker density used in QTL mapping research in history, not all genetic variations of these traits have been captured , in the PLEC, PLEKHA5, TONSL, PTGER4, and LCORL genes showed pleiotropic effects on milk production or body conformation traits. In addition, some important candidate genes, including GPAT3, CEBPB, AGO2, SLC37A1, and FNDC3B, were also found to be related to the fat metabolism or involved in mammary gland development. The above genes however need to be consolidated as new potential genes through future validation.A total of 95 significant SNPs were identified to be related to the milk production, somatic cell score, and body conformation traits in Holstein cattle. Among them, 7 significant SNPs located on the https://bigd.big.ac.cn/gvm/getProjectDetail?project=GVM000388. The Bioproject accession number is PRJCA011726. Further inquiries can be directed to the corresponding author.The original contributions presented in the study are included in the article or supplementary material, the variation data reported in this article have been deposited in the Genome Variation Map (GVM) in Big Data Center, Beijing Institute of Genomics (BIG), and Chinese Academy of Sciences, under accession numbers GVM000388 that are publicly accessible at The animal study was reviewed and approved by Animal Care Advisory Committee, Northeast Agricultural University .ZW, PW, and XL conceived the study and participated in its design. YZ, JW, QK, and XN were involved in the acquisition of data. XL, JW and QZ performed all data analysis. XL and ZW drafted the manuscript. ZW, PW, XL, YZ, CZ, QK, and XN contributed to the writing and editing. All authors read and approved the final manuscript.This work was financially supported by the Natural Science Foundation of China (No. 32070571), the Academic Backbone Project of Northeast Agricultural University (No. 15XG14), and the NEAU Research Founding for Excellent Young Teachers (2010RCB29).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.