Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses.Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as NCT00390637.Diet, Obesity and Genes (DiOGenes) trial registration number:  Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants  affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level. Howev effects –13, whiln = 1 644) [n = 789) [In this paper, we demonstrate that both concerns can be addressed using statistical approaches and data tailored to the problem under consideration: we present an integrative genome-wide pQTL study of two clinical cohorts performed with our Bayesian joint QTL method LOCUS , which s= 1 644)  for discn = 789) . Each dan = 789) ) assays,Our work aims to illustrate the utility and feasibility of multivariate Bayesian QTL analyses. Notably, we show the gain in statistical power achieved by LOCUS on our data by confronting the validated hits with those of a classical two-stage univariate design. We also assess the performance of both approaches in simulations mimicking real data conditions.https://locus-pqtl.epfl.ch/db.Pertinent interpretation of pQTL loci in the context of complex diseases hinges on a careful examination of metabolic and clinical parameters from the same subjects or, at a minimum, from a population presenting similar clinical characteristics. Using comprehensive data from the two pQTL cohorts, we evaluate the enrichment of the proteins under genetic control for associations with clinical parameters; this assessment suggests that our pQTL findings may ease the identification of proteomic biomarkers and calls for further dedicated research. All results are available from our online browser We analyzed the pQTL data from the Ottawa and DiOGenes cohorts in a two-stage study. The DiOGenes cohort recruited overweight/obese, non-diabetic subjects, while the Ottawa study was led in a specialized obesity practice where subjects had severe obesity, dyslipidemia and insulin resistance disorders .We used LOCUS , Materia for joincis and trans effects. With the MS data, all 15 cis Ottawa pQTLs replicated in DiOGenes, while the 3 trans pQTLs did not. With the SomaLogic data, 78 of 81 cis and 20 of 37 trans pQTLs could be validated  of LOCUS two-stage analysis to those obtained by re-analyzing the Ottawa and DiOGenes datasets with GEMMA.There is a broad consensus in the biostatistical community about the benefits of collectively accounting for all SNPs while leveraging information across multiple correlated outcomes –22. Howeechoseq. A comparison of the synthetic and real proteomic levels is provided in Simulation studies can be far from reflecting real data scenarios. To best emulate our study and obtain a realistic assessment of the relative performance of the multivariate and univariate approaches, special care was taken to use synthetic data that resemble the real data and embody accepted principles of population genetics . In particular, we employed real SNPs from our study as candidate predictors and we simulated proteomic levels by replicating the block-correlation structure and variability of the 133 MS protein levels, using our freely available data generator The first simulation study uses the SNPs from chromosome one of the Ottawa cohort and the 133 outcomes mimicking the MS proteomic levels, enforcing that the SNPs explain together at most 35% of each protein variance . The ROC curves  show a nBy design, univariate screening approaches do not exploit association patterns common to multiple outcomes or markers; they analyze the outcomes individually, and do not account for linkage disequilibrium (LD), which often results in a number of redundant proxy discoveries at loci with strong LD . At a giωs .We now compare LOCUS and GEMMA on the real data under the typical two-stage GWA scenario employed in pQTL studies , 8, 9. Ntrans associations and 6/15 hits were not previously described in the literature using a r2 > 0.5-proxy search . LOCUS could successfully identify all 6 locus-protein hits with very high posterior probabilities of inclusion (PPI >0.99). The sentinel SNPs selected by LOCUS are reported in the “LOCUS extended panel” column of Finally, LOCUS missed 6 locus-protein hits identified by GEMMA, but, importantly, these hits were not detectable by LOCUS. Indeed, all 115 SNPs selected by GEMMA had been removed by the trans signals , novel signals  and have successful cross-assay validation using the MS and SomaLogic measurements of their protein if available .In summary, the improvement, seen in the simulation study, of the multivariate design over the classical univariate design is confirmed on the real data: LOCUS achieves higher replication rates, uncovers 15 pQTL loci missed by univariate screening . Among the hits missed by the univariate design but validated by LOCUS two-stage analysis, several are ABO and a novel locus on chromosome 11 trans-acting on the XRCC6 levels. These two examples are representative of the types of signals which LOCUS tends to better detect, namely weaker signals, possibly involving a shared control from a single locus. We also formulate some hypotheses on the involvement of these pQTLs in metabolic health, to be explored in dedicated studies.In this section we examine the advantages of our multivariate analysis for the detection of two loci, i.e., the notorious pleiotropic locus ABO signals by LOCUS was facilitated by its tailored parametrization that directly models pleiotropy via ωs, as discussed in the simulation section and shown in α = 0.2.The recovery of the ABO is a well-known pleiotropic locus associated with coronary artery diseases, type 2 diabetes, liver enzyme levels  and lipid levels [ABO region: rs2519093 and rs8176741 (r2 = 0.03). The former SNP is trans-acting on E-selectin (protein LYAM2 encoded by SELE), the Insulin Receptor and the CD209 antigen. The latter SNP is trans-acting on the Tyrosine-protein kinase receptor (Tie-1), Cadherin-5 and CD209. Both SNPs were reported as cis-acting eQTL variants for ABO, OBP2B and SURF1, and further queries in public databases indicated that rs8176741 may affect the binding sites for three transcription factors , suggesting a complex gene regulation circuitry.d levels , 5, 7. OABO locus might have a role in metabolic health, although further research is needed to confirm this. In particular, the CD209 protein could be a secreted protein, released by M2 macrophages from adipose tissue, with a beneficial role in controlling lipid levels, thereby possibly protecting from developing dyslipidemia and related metabolic complications; see Our analyses of the clinical parameters measured in the Ottawa and DiOGenes studies found significant associations of the controlled proteins with several clinical variables, such as triglycerides, visceral fat, fasting insulin, fasting glucose and HDL ; this suABO locus [SELP), MET, NOTC1, OX2G (CD200), TIE2 (TEK), VGFR2 (KDR), VGFR3 (FLT4), VWF. Multiple reasons could explain this lack of significance, including but not only: the use of samples from the obese population where these signals may be absent, a lack of statistical power and/or a poor quality of the SomaLogic measurements. For TIE2 and VGFR3, LOCUS reported very weak signals (PPI <0.1) suggesting insufficient power. As for the other absent signals, the hypothesis of poorly quantified SomaLogic levels is not unlikely but difficult to assess since all seven proteins were measured only in the SomaLogic assay only and could therefore not be compared to corresponding MS measurements.Finally, some proteins had been reported as associated with the BO locus  and yet trans pQTL for the XRCC6 levels . Proxy searches (down to r2 = 0.5 in European ancestry panels) did not reveal any tag SNP previously reported as a QTL . Again, the univariate screening results indicated some trans signal between rs4756623 and XRCC6, but the effect did not survive the multiplicity correction (Our second example concerns the variant rs4756623 identified by LOCUS as a novel rrection .XRCC6 gene activates DNA-dependent protein kinases (DNA-PK) to repair double-stranded DNA breaks by nonhomologous end joining. DNA-PKs have been linked to lipogenesis in response to feeding and insulin signaling [XRCC6 functions have been reported as associated with regulation of beta-cell proliferation, islet expansion, increased insulin levels and decreased glucose levels [The ignaling . DNA-PK ignaling . A recenignaling . It desce levels , 32.LRRC4C gene a binding partner for Netrin G1, which has a known role in metabolism . Forty-nine of these 77 SNPs were eQTL variants for the gene coding for the protein with which they were associated in our datasets. Our pQTLs were also enriched in epigenome annotation marks (p = 9.20 × 10−4) and significantly closer to transcription start sites compared to randomly chosen SNP sets (p = 9.99 × 10−6). These observations suggest potential functional consequences for our pQTL hits.We assessed the overlap of the 113 validated pQTLs with known eQTLs . Seventyr2 > 0.8). Nineteen SNPs were directly involved in these associations (p < 2.2 × 10−16).A total of 217 previously reported genome-wide associations overlapped our validated pQTL loci, corresponding to 139 unique traits mapping to 68 distinct regions , with this background adjustment accounting for the bias of SomaLogic proteins towards specific inflammation and oncology-related pathways  highlighted in previous studies , 6–9, deABO and a novel locus trans-acting on XRCC6, and can be particularly important on datasets with small sample sizes.This corroborates our extensive simulation studies, which demonstrated the improved QTL detection of LOCUS compared to univariate approaches on synthetic data mimicking the real data. In particular, these experiments indicated that the joint modelling of all outcomes leads to a significant increase of power in scenarios where a shared regulatory architecture governs groups of molecular levels. They also showed that accounting for local linkage disequilibrium using simulated annealing further enhances the detection of weak effects and drastically reduces the multiplicity burden. These advantages were exemplified for the pleiotropic locus https://locus-pqtl.epfl.ch/db.Finally, our analyses indicated that proteins under genetic control are enriched in associations with metabolic parameters, which supports a genetic basis of these parameters and emphasizes the advantages of pQTL studies for elucidating their underlying functional mechanisms. Evaluating the genetic control of plasma protein levels has the potential to reveal circulating biomarkers of direct interest for diagnostic applications but clearly, large pQTL studies in adipose tissues or liver may provide further, more specific insights on the role of the proteome in metabolic health. Although still at their early stages, pQTL studies are also intrinsically limited by the invasive nature of accessing tissues in humans. We hope that future progress in the availability of tissue-specific proteomic samples will reveal new mechanisms unavailable from plasma studies. As it is likely that sample sizes will remain limited, our well-powered joint approach will be a useful tool to this end. In the meantime, our complete plasma pQTL and clinical association results offer opportunities to study functional mechanisms and possible therapeutic options; they are accessible from the searchable online database Our work presents a direct illustration of a fully joint QTL analysis at scale and highlights concrete biological findings that take advantage of our tailored statistical approach. A central ambition was to showcase that LOCUS can bridge the gap between Bayesian multivariate inference and its practical use for analyzing current molecular QTL data. Indeed, the applicability of LOCUS goes beyond pQTL studies, as it is tailored to any genomic, proteomic, lipidomic, metabolic or methylation QTL analyses and can be used for genome-wide association with several clinical endpoints. The computational burden involved in most Bayesian inference approaches has hampered the use of multivariate hierarchical modelling on large molecular QTL datasets thus far; our joint framework overcomes this with a scalable batch-wise variational algorithm and an effective C++/R implementation. It therefore offers a direct alternative to univariate screening approaches whose drawbacks for uncovering weak QTL effects are the object of a broad consensus –13. PerfOttawa and DiOGenes studies were approved by the local human research ethic committees. Participants provided informed written consent, and all procedures were conducted in accordance with the Declaration of Helsinki. Trial registration number: NCT00390637. Registered 20 October 2006.The The Ottawa study was a medically supervised program set up by the Weight Management Clinic of Ottawa . Subject2), non-diabetic and otherwise healthy.The DiOGenes study was a multi-center pan-European program . Eight pFor both studies, subjects who were not under fasting conditions at plasma sample collection were excluded from the proteomic analyses. The main clinical characteristics of the cohorts are given in Plasma protein expression data were obtained using two types of technologies: mass-spectrometry (MS) and a multiplexed aptamer-based assay developed by SomaLogic . Samples2-transformation of the protein ratio fold changes with respect to their measurements in the biological plasma reference sample. The sample preparation and all other manipulations relative to the MS measurements are detailed further in previous work [The MS proteomic quantification used plasma samples spiked with protein standard lactoglobulin (LACB). Samples were immuno-depleted, reduced, digested, isobarically 6-plex labeled and purified. They were analyzed in duplicates on two separate but identical systems using linear ion trap with Orbitrap Elite analyzer and Ultimate 3000 RSLCnano System (Thermo Scientific). Protein identification was done with the UniProtKB/Swiss-Prot database , using Mous work –36.2-transformed. The SomaLogic panel targetted 1, 129 proteins; the KEGG and Reactome pathways covered by this panel are listed in The SomaLogic protein measurements were characterized using the SOMAscan assay , which rWe discarded MS-based proteins if their measurements were missing for more than 5% of the samples, leaving 210 proteins in the Ottawa cohort and 136 in the DiOGenes cohort; we restricted all downstream analyses to the 133 proteins available for both cohorts. The SomaLogic measurements had no missing values. Totals of 1, 100 and 1, 129 proteins were assayed in the Ottawa and DiOGenes cohorts. All our analyses focused on the 1, 096 proteins quantified for both cohorts. The overlap between the MS and SomaLogic panels was of 72 proteins for 397 and 389 subjects in Ottawa and DiOGenes respectively.q1 − 3 × IQR, q3 + 3 × IQR, where q1, q3 are the lower and upper quartiles, and IQR is the interquartile range). After this quality control procedure, 577 and 428 Ottawa samples remained in the MS and SomaLogic datasets, respectively, and 481 and 563 DiOGenes samples remained in the MS and SomaLogic datasets, respectively.We excluded samples with extreme expression values in more than 5% of the proteins, i.e., values beyond the outer fences of the empirical distribution , according to their manufacturer’s instructions and were called with the GenomeStudio Software provided by Illumina. Preprocessing steps, including imputation and quality control, have been previously documented . We discp = 275, 485 tag SNPs remained for the SomaLogic analysis and p = 275, 297 tag SNPs remained for the MS analysis. As SNPs were imputed, the 0.95-r2 pruning led to a drastic cut of “redundant” markers: without this pruning step, the number of SNPs was ≈4M. Such a reduction is not surprising considering the nature of the underlying SNP arrays  and indicates that little information was discarded. In the Ottawa cohort n = 376 subjects had both genotype and MS proteomic data, and n = 394 subjects had both genotype and SomaLogic proteomic data. In the DiOGenes cohort, these numbers were n = 400 and 548.The above steps were performed separately for the Ottawa and the DiOGenes cohorts, so in order to define a common set of SNPs for discovery and replication, we restricted each dataset to the SNPs available for both cohorts. After all genetic quality controls, and in both cohorts, visceral adiposity index (VAI) values using the formula of Amato et al. [Both cohorts had records on age, gender, anthropometric traits (weight and BMI), glycemic variables , and total lipid levels obtained from blood biochemistry . We derived LDL values using the Friedewald formula , and obto et al. . In eachy =  is an n × q matrix of q centered outcomes , and X is an n × p matrix of p centered candidate predictor SNPs, for each of n samples. Each outcome, yt, is related linearly to all p candidate SNPs, and has a specific residual precision, τt, to which we assign a Gamma prior, τt ∼ Gamma. The association between each pair of SNP and outcome is modelled using a spike-and-slab prior, namely, for s = 1, …, p and t = 1, …, q,δ0 is the Dirac distribution. Hence, to each regression parameter βst corresponds a binary latent parameter γst, which acts as a “predictor-outcome association indicator”: the predictor Xs is associated with the outcome yt if and only if γst = 1. The parameter σ represents the typical size of nonzero effects and is modulated by the residual scale, σ−2 ∼ Gamma. Finally, we let the probability parameter ωs have a Beta distribution,as and bs are set so as to enforce sparsity as described in Ruffieux et al. [γs1, …, γsq, the parameter ωs controls the proportion of outcomes associated with the predictor Xs, and hence directly represents the propensity of predictors to be pleiotropic “hotspots”. Both ωs and σ2 allow the leveraging of shared association patterns across all molecular variables, which enhances the estimation of QTL effects; see the graphical representation of the model , and generated associations between 20 SNPs and 25 proteins chosen randomly, leaving the remaining variables unassociated. Some proteins were under pleiotropic control; we drew the degree of pleiotropy of the 20 SNPs from a positively-skewed Beta distribution, so only a few SNPs were hotspots, i.e., were associated with many proteins. We generated associations under an additive dose-effect scheme and drew the proportions of outcome variance explained by a given SNP from a Beta distribution to give more weight to smaller effect sizes. We then rescaled these proportions so that the variance of each protein attributable to genetic variation was below 35%. These choices led to an inverse relationship between minor allele frequencies and effect sizes, which is to be expected under natural selection. We generated 50 replicates, re-drawing the protein expression levels and effect sizes for each. On top of comparing the relative performances of LOCUS and GEMMA on these data, we also illustrated the benefits of modelling all the proteomic outcomes jointly by comparing the performance of LOCUS when applied to increasingly larger batches of proteins. Namely, we split randomly the proteins into batches of given sizes—ranging from 1, for a univariate modelling of the proteins, to all 133 MS proteins, for a fully multivariate treatment corresponding to a classical application of LOCUS—and ran LOCUS separately on each batch and all SNPs from chromosome one. We then aggregated posterior probabilities of inclusion to assess selection performance. This procedure was repeated for each of the 50 synthetic data replicates.For the first simulation, we ran LOCUS and GEMMA on the SNPs of all p = 2, 000 SNPs, and ran LOCUS and GEMMA on 20 replicates for each configuration.For the second simulation, we re-assessed the performance of LOCUS for a grid of data generation scenarios. We considered a wide range of sparsity levels (numbers of proteins under genetic control) and effect sizes (proportions of outcome variance explained by the genetic variants). Given the large number of configurations (130), and in order to limit the computational burden, we used the first We performed pQTL analyses separately for each platform, i.e., one analysis for the MS proteomic dataset, and another for the SomaLogic proteomic dataset. Each analysis comprised two stages: a discovery stage using the Ottawa cohort and a replication stage based on the DiOGenes cohort.−3 on the absolute changes in the objective function as the stopping criterion. The algorithm can handle missing data in the outcome matrix, so no imputation was necessary for the MS proteomic data.For discovery, we used LOCUS on both the MS and the SomaLogic datasets, with an annealing schedule of 50 geometrically-spaced temperatures and initial temperature of 20; pilot experiments indicated that estimation was not sensitive to these choices. We used a convergence tolerance of 10We adjusted all analyses for age, gender, and BMI. No important stratification was observed in the genotype data; the first ten principal components together explained little of the total variance (<4%), so we did not include them as covariates. We derived FDR values from the posterior probabilities of association obtained between each SNP and each protein, and reported pQTL associations using an FDR threshold of 5%.p-values from (two-sided) Wald tests. We then obtained adjusted p-values using Benjamini–Hochberg FDR, and validated our hits using again an FDR threshold of 5%.We performed a validation study of the pQTLs discovered using the DiOGenes cohort with GEMMA , 47, witTo assess the extent to which LOCUS two-stage pQTL analysis discovers more hits than the univariate procedures routinely applied for e- or pQTL analyses, we re-performed the entire study using GEMMA , 26. We To account for proxy hits arising from the SNP LD structure and provide grounds for comparison between GEMMA and LOCUS, we defined hits at the level of loci as follows: the hits identified by GEMMA and/or LOCUS as associated with a same protein (quantified by the same proteomic technology) were considered to be in a same locus if there was no more than 1Mb between two consecutive hits. The additional hits found by GEMMA at Bonferroni level 0.2 were assigned to the closest existing loci (mapping to the same protein), provided that the distance was less than 1Mb; new loci were defined for the remaining hits.sentinel SNP , and also listed the SNPs in LD (r2 > 0.8), limiting the search to 500Kb upstream and downstream of the sentinel SNP position. We called cis pQTLs, all sentinel SNPs located within ±1Mb of the gene encoding for the controlled protein, and trans pQTLs, all other pQTLs.We used the Ensembl database  . As queries using the R package phenoscanner are limited to 500 returned tuples, we downloaded a local copy of the database (retrieved on 26/03/2019). Moreover, since protein names in the database do not follow the official UniProt protein names, we retrieved the annotation files from all individual studies and remapped the protein names onto the official UniProt identifier, thereby enabling the comparison with our pQTL hits using dbSNP rsIDs and UniProt IDs.We evaluated the overlap between our pQTL associations and previously reported pQTL signals with the PhenoScanner database , 50, usi5 times to derive empirical p-values.We retrieved epigenomic annotations of 1, 000 Genomes Project (release 20110521) from Pickrell . The datq-value <0.05. We considered all 49 tissues listed by GTEx but eQTL SNPs for several tissues were counted only once. We made both general queries and queries asking whether a pQTL uncovered by LOCUS was an eQTL for the gene coding for the controlled protein.We evaluated the overlap of our pQTLs with the eQTL variants reported by the GTEx Consortium , 54 (relr2 > 0.8).We retrieved known associations between the validated sentinel pQTLs and diseases or clinical traits, based on the GWAS catalog , 56 , and evaluated the directional consistency of the effects between Ottawa and DiOGenes. We adjusted for multiplicity using Benjamini–Hochberg correction across all tests, i.e., involving the 88 tested proteins and the two proteomic technologies, and reported associations using a 5% FDR threshold.We tested associations between the proteins under genetic control and clinical parameters separately in each cohort. For the DiOGenes data, we used linear mixed-effect models, adjusting for age, gender as fixed effects, and center as a random effect. For the Ottawa data, we used linear models, adjusting for age and gender. Except when testing associations with anthropomorphic traits, all analyses were also adjusted for BMI. For the clinical variables available in the two cohorts , we performed meta-analyses using the R package metafor . We used5 sets of 88 proteins from the panel used for the pQTL analyses and derived an empirical p-value by counting, for each set, the number of proteins with at least one clinical association at FDR 5%.We assessed whether the proteins under genetic control were enriched in associations with the clinical variables. We randomly selected 10https://github.com/hruffieux/echoseqECHOSEQ: http://grch37.ensembl.org/index.htmlEnsembl: http://www.xzlab.org/software.htmlGEMMA: https://gtexportal.org/homeGTEx: https://www.ebi.ac.uk/gwasGWAS Catalog: http://mathgen.stats.ox.ac.uk/impute/impute_v2.htmlIMPUTE2: http://jaspar.genereg.netJASPAR: https://github.com/hruffieux/locusLOCUS: https://cran.r-project.org/web/packages/metafor/index.htmlMetafor: http://www.phenoscanner.medschl.cam.ac.ukPhenoScanner: http://zzz.bwh.harvard.edu/plinkPLINK: http://www.proteomexchange.orgProteomeXchange: https://www.r-project.orgR: https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.htmlSHAPEIT: https://ccg.vital-it.ch/cgi-bin/snp2tfbs/snpviewer_form_parser.cgiSNP2TFBS: https://genome.ucsc.eduUCSC: https://www.uniprot.orgUniProt: S1 AppendixScatterplots for the 72 protein levels with dual-technology quantification for both the Ottawa and the DiOGenes cohorts.(PDF)Click here for additional data file.S2 AppendixEmulation of the MS QTL data; Runtime profiling for different problem sizes.(PDF)Click here for additional data file.S3 Appendixp-value threshold.Six examples of pQTL loci found by LOCUS and missed by the univariate analysis; Sensitivity of the univariate analysis to the (PDF)Click here for additional data file.S4 AppendixABO. Clinical associations with the XRCC6 protein levels. CFAB and RARR2, mediators of adipogenesis are under genetic control; The importance of IL1AP for Metabolic Syndrome; WFKN2, a TGFβ-activity protein with protective effect against metabolic disorders; Inflammation mediated proteins and their role in insulin resistance; Complement/coagulation: a trans-acting insertion linking PROC and its receptor.Clinical associations with proteins controlled by the pleiotropic locus (PDF)Click here for additional data file.S5 AppendixDetailed description of the quality control performed on the genotyping data for the Ottawa and DiOGenes studies.(PDF)Click here for additional data file.S1 TableAverage anthropometric, glycemic and total lipid characteristics of the Ottawa and DiOGenes cohorts.(XLSX)Click here for additional data file.S2 TablepQTL associations discovered by LOCUS in the Ottawa cohort, using the mass-spectrometry and SomaLogic datasets.(XLSX)Click here for additional data file.S3 TablepQTL associations discovered by LOCUS in the Ottawa cohort and validated in the DiOGenes cohort.(XLSX)Click here for additional data file.S4 TableOverlap of the validated pQTL associations with public pQTL associations (PhenoScanner).(XLSX)Click here for additional data file.S5 TableComparison with the univariate two-stage analysis using GEMMA, individual hits.(XLSX)Click here for additional data file.S6 TableComparison with the univariate two-stage analysis using GEMMA, hits summarized by loci.(XLSX)Click here for additional data file.S7 TableOverlap of the validated pQTL associations with public eQTL associations (GTEx Consortium).(XLSX)Click here for additional data file.S8 TableOverlap of the validated pQTL associations with disease risk loci .(XLSX)Click here for additional data file.S9 TableAssociations between proteins under genetic control and clinical parameters measuring dyslipidemia, insulin resistance and visceral fat, in the Ottawa and DiOGenes cohorts.(XLSX)Click here for additional data file.S10 TableEnrichment analysis using KEGG (2019) and Reactome (2016).(XLSX)Click here for additional data file.


P < 1.655E−06). This study characterized the genetic distance and relationship of tea collections, suggested the core collections, and established an efficient GWAS analysis of GBS result.An accurate depiction of the genetic relationship, the development of core collection, and genome-wide association analysis (GWAS) are key for the effective exploitation and utilization of genetic resources. Here, genotyping-by-sequencing (GBS) was used to characterize 415 tea accessions mostly collected from the Guizhou region in China. A total of 30,282 high-quality SNPs was used to estimate the genetic relationships, develop core collections, and perform GWAS. We suggest 198 and 148 accessions to represent the core set and mini-core set, which consist of 47% and 37% of the whole collection, respectively, and contain 93–95% of the total SNPs. Furthermore, the frequencies of all alleles and genotypes in the whole set were very well retained in the core set and mini-core set. The 415 accessions were clustered into 14 groups and the core and the mini-core collections contain accessions from each group, species, cultivation status and growth habit. By analyzing the significant SNP markers associated with multiple traits, nine SNPs were found to be significantly associated with four leaf size traits, namely MLL, MLW, MLA and MLSI ( Camellia sinensis) with connected species, all of which belong to Thea of genus Camellia in the family Theaceae (n = 2x = 30) with a genome size of 3.02 Gb and is an endemic species in southwest China  O. Ktze, 100 C.tachangensis F.C. Zhang, 59 C. remotiserrata Zhang, and five near C.taliensis W.W. Smith were identified  were used for this study . Based oentified . Hereaftentified . The anc2, and kept at −80 °C until use (One hundred and sixty-eight samples were collected from areas suitable for tea growth in north Guizhou (Ia) , 51 sampntil use .Genomic DNA was extracted from the plant samples with a kit for rapid extraction of genomic DNA. DNA integrity was tested by 1% agarose gel electrophoresis; a Qubit Fluorometer was used to check the purity and measure the concentration of DNA samples .Sac I and Mse I (NEB) in a 25 μl reaction containing 1 × restriction buffer. The resulting samples were ligated with the restriction fragment using the SacAD and MseAD adaptors, which contained different barcode combinations to distinguish the samples. Equal volumes of the ligated products of 12 individuals were pooled and processed using the QIA quick PCR Purification Kit (Qiagen). PCR was performed using PCR Master Mix and the PCR Primer Cocktail, which could enrich DNA fragments with the adapters. The PCR products of each mixture were pooled and separated on a 2% agarose gel by electrophoresis. Fragments of 500 to 550 bp (including the 120 bp adaptor) were recovered with the QIA quick Gel Extraction Kit (Qiagen). The average length of DNA fragment was selected for final library construction with the Agilent DNA 12,000 kit using the 2100 Bioanalyzer system. To quantify the final library, quantitative real-time PCR with a Taq Man probe was employed. Then the libraries were sequenced on the IlluminaHiSeq X Ten platform based on the paired-end 150 (PE150) strategy. Each library contains 48 samples, and clean data were then parsed into different units that exactly match the barcodes and the restriction sites at both ends (One-hundred nanogram genomic DNA was double digested with 5 U of oth ends .http://www.plantkingdomgdb.com/tea_tree/)   using BW0 -k 30’ . SNPs an2 threshold of 0.2 were used to prune the SNPs using Plink (v1.9). As a result, a set of 415 accessions and 30,282 high-quality SNPs were retained and used for subsequent analyses  and mature leaves width (MLW) of each individual and calculated their means to represent the traits of MLL and MLW, respectively. Leaf size measurements were performed in Spring (monthly average temperature is 14.68 °C) and Fall (monthly average temperature is 21.29 °C), 2018, seperately. Leaf shape index (MLSI), calculated as MLL/MLW, was the third trait we measured. The fourth trait, mature leaf area (MLA), was calculated as 0.75 × MLW × MLL. We modified our data collection method according to guidelines of UPOV  for tea. Statistical analysis of the phenotypic data was conducted using Microsoft Excel 2010 and SPSS 15.0 software. Pearson’s correlation coefficients (r) were calculated for the four traits. Means, standard deviations, and ranges were calculated for trait distribution. To evaluate whether the data followed a normal distribution, Skewness and Kurtosis were calculated using the Descriptive Statistics model-based Frequencies distribution of analysis implemented in SPSS15.0 software.Pi and Pj were the frequencies of the number of i and j alleles respectively, and n was the number of alleles  values for the SNP data .  alleles . We can The alleles, genetic distance and the observed heterozygosity (Ho) were calculated on every groups with TASSEL v.5.2.37 . The neiAs subsets of larger genetic collections, core collections contain the smallest number of accessions that represent the maximum diversity of the raw collection. DARwin (v.6.0. 17) was used to construct the diversity trees . We usedP-value in the peak area , it is considered significantly associated with the targeted trait. Correlation coefficient (R2) explained the phenotypic variation by every marker-trait association analyzed.Association analysis was carried out on the four measurable traits, namely MLL, MLW, MLSI, and MLA, in two seasons, independently. We used the mixed linear model (MLM) implemented in TASSEL (v.5.2.37) because the GLM model has a high false positive rate  followinC. sinensis . The second group contained 88 accessions, most of which belong to C. tachangensis  and wild type . The third group consisted of 54 accessions, most of which were wild type  and C. remotiserrata . Group four had a total of 76 accessions and most were cultivation type , modern landraces  and C. sinensis . Group five had 54 accessions, all of which were from C. sinensis and cultivation type, and the number of modern landraces were almost the same as that of the ancient landraces. Groups six, seven, eight, 12, 13 and 14 consisted of 15, 15, two, seven, one and one accessions, respectively, all of which were ancient landraces, cultivation type, and C. sinensis varieties. Groups nine and ten consisted of three and six accessions, respectively, which were wild tree and C. remotiserrata. Group 11 had a total of 20 accessions and most were cultivation type , ancient landraces  and C. sinensis   .x-axis, indicating no significant difference in the information of the 104 accessions, suggesting that the elimination of these accessions had no significant impact on sphericity index , 1,403 (4.93%), and 1,722 (6.04%), respectively . Ho, PICP < 0.0001), as revealed by the Pearson correlation analysis (P < 0.0001) between two environments   . Among t655E−06) .For MLW, only one significant SNP was detected on the same locus as MLL, which explained 8.24% of the phenotypic variation; this SNP has a negative major allele effect of −1.31 and a positive minor allele effect of 0.44 for MLW. Six loci, located at Sc0000000, Sc0000365, Sc0002172, Sc0002452, Sc0004093 and xfSc0000122, significantly correlated with MLA, explaining 3.49% to 4.50% of the total phenotypic variation, with the main influence varying from -18.23% to 4.462% and minor effects varying from -18.76% to 42.47% . Three SGBS is a cost-efficient and effective genotyping method . In thisCamellia sinensis and related species are cross-pollinated species with a low self-pollination rate ranging from 2% to 6%  and P represents P-matrix from the top three PCs, that are used as covariates in GWAS. K refers to kinship matrix and is used to determine the correlations between individuals. Both the Q + K and P + K matrices fit the MLM to control spurious associations resulted from relatedness and population structure, separately (MLM was employed for association mapping and two models (MLMQ+K and MLMP+K) to control false positive caused by population structure. Leaf size is associated with local adaptation, and it can actually decrease the false rate by removing accessions with extreme genetic diversity and phenotypic expression . We did parately .qLSI13) for leaf size in tea plant. In this study, nine SNPs significantly associated with leaf traits were identified using the P+K MLM. Six, three, two and one SNPs were significantly associated with MAL, MLSI, MLL and MLW, respectively. SNP PG_1:32346865 was significantly associated with three traits, MLL, MLW and MLA, all were significantly positively correlated (P < 0.0001). Which was consistent with the results of significant positive correlation among the three characters .Nucleotide codes are as follows: A, adenine; C, cytosine; G, guanine; T, thymine; Y, C or T; R, A or G; K, G or T; M, A or C; W, A or T; S, G or C , the core collection (N = 198) and the mini-core collection (N = 148).The accession/clone /collection, the accession name, the zone, the cultivation status, growth habits, the species, the observed heterozygosity, the core collection (Click here for additional data file.10.7717/peerj.8572/supp-8Table S2Click here for additional data file.10.7717/peerj.8572/supp-9Table S3Click here for additional data file.10.7717/peerj.8572/supp-10Table S4Click here for additional data file.10.7717/peerj.8572/supp-11Table S5Click here for additional data file.10.7717/peerj.8572/supp-12Table S6Click here for additional data file.10.7717/peerj.8572/supp-13Table S7Click here for additional data file.


Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits. Electronic Health Records (EHRs) have emerged as an abundant data source for deriving phenotypes used in genetic association studies. EHRs provide a broad range of clinical data in large health system cohorts and are readily incorporated into large-scale meta-analyses. The abundance of available data in EHRs introduces unique technical challenges, particularly longitudinal clinical lab measurements which lack the structure of more commonly used disease diagnosis codes. Conflicting strategies exist in the literature and it is not clear how portable these strategies are across health systems. In this study we performed a proof-of-principle meta-analysis of 70 clinical lab traits in two large-scale health systems: BioVU from Vanderbilt University and the Michigan Genomics Initiative from Michigan Medicine. Despite the challenges of matching labs across the two health systems, we observed a high replication rate for known genetic variants. Further, we identified 31 novel associations, 22 of which replicated in an independent BioVU cohort, indicating the potential for future meta-analyses. Finally, we explored the impact of various analytic strategies, looking for consistent effects between our two cohorts, to determine optimal strategies for future genetic analysis of EHR-derived lab traits. Laboratory testing is a key component of modern medicine. Laboratory measurements provide a glimpse into the functioning of the human body, allowing clinicians to diagnose and monitor disease. In most health systems, lab measurements are routinely captured in patient Electronic Health Records (EHRs) alongside disease diagnoses, free text notes and medical procedures to provide a detailed, longitudinal health history . EHRs prPSCK9 was initially discovered based on quantitative LDL measurement rather than clinical diagnosis [Several large-scale genetic studies have already leveraged biobanks linked to EHRs, such as the UK Biobank , Japan Biagnosis . In contiagnosis –13. HereThe rich source of quantitative lab measurements in EHR cohorts comes with unique concerns. Quantitative traits collected specifically for research purposes typically use a controlled experimental design to ensure consistency among samples. In contrast, lab measurements contained in EHRs are a historical record of medical care. As such, patients may have hundreds of lab measurements for some traits and none for others, depending on their specific health history and utilization of the health system. The measurements can be collected in times of sickness or good health leading to substantial variation in measurements for the same lab. Lab measurements can also be artificially modified by prescription medication, such as statin use for lowering LDL cholesterol. Moreover, recruitment mechanisms and health system demographics can dramatically shape the overall health of the biobank cohort, which in turn dictates lab measurements available for analysis. The broad impact of using such “real world” measurements for genetic association studies is unclear. Questions remain over the effect and robustness of analytic choices made when analyzing EHR-based lab traits including how best to summarize complicated, longitudinal lab measurements and whether comorbid diseases highly correlated with lab measurements must be considered. Prior studies are not consistent in addressing these concerns. For example, GWAS of EHR-derived quantitative traits in Biobank Japan enrolled patients with at least 1 of 47 diagnoses and controlled for all 47 diagnoses while testing each lab . In contIn this paper we explore strategies for analyzing quantitative lab measurements extracted from EHRs and describe the first large-scale meta-analysis of EHR-derived lab traits across independent health systems. We used lab measurements and genetic data from two academic health systems: the BioVU cohort from Vanderbilt University  and the Our initial challenge was identifying which labs to meta-analyze between the health systems. Accurately matching labs is complicated by the fact that no standardized coding scheme exists for lab measurements. Dichotomous disease traits are readily matched between health systems using the ubiquitous ICD coding system for disease diagnoses . AlthougThe meta-analysis required several strategic choices regarding data preparation and statistical analysis. We explored the consequences of various analytic choices using a series of mirrored analyses performed in MGI and BioVU. In particular, we varied the summary statistic for lab measurements and the inclusion of covariates to control for comorbid diseases in the GWAS. We compared the results between the independent biobank cohorts to assess consistency of effects. We hypothesized that alternative summary statistics to the basic mean could provide more powerful genetic analyses. We considered: the median lab measurement due to robustness against data recording errors and extreme measurements, the first available lab measurement to mitigate the effects of prescription drugs on modifiable lab traits, and the maximum recorded measurement to magnify variation in extreme measurements. The comorbidity analysis compared GWAS results from models that included indicator covariates for a wide array of diseases to models that did not.PheWeb web browser [http://pheweb.sph.umich.edu/mgi-biovu-labs and available for bulk download at https://phewascatalog.org/labwas and ftp://share.sph.umich.edu/mgi_biovu_labwas/.The complete set of GWAS summary statistics from this analysis are broadly available to the research community. We encourage others to use this data to replicate their own GWAS findings and perform hypothesis-driven lookups on specific SNPs or lab traits of interest. Our results are viewable through an interactive  browser  at http:We analyzed data from two university hospital biobanks that link electronic health records with genetic data: BioVU from Vanderbilt University and the Michigan Genomics Initiative (MGI) from Michigan Medicine. We restricted our analysis to unrelated patients of European ancestry because of insufficient patient sample sizes and a paucity of known variants in non-European populations.The BioVU cohort has been described previously . BrieflyThe MGI cohort has also been described previously . BrieflyWe extracted all available clinical lab measurements and metadata from the electronic health records of MGI samples and BioVU samples. We collapsed distinct labs when obvious duplications were present . Available metadata differed slightly between the health systems but included brief text descriptions, unit of measurements, and range for normal values. We excluded individual lab measurements taken outside the health system labelled as “External.” In cases where multiple tests examined the same analyte, e.g. blood glucose, we removed point of care (POC) tests which are more susceptible to technical artifacts and tend to be deployed in intensive care or emergency settings where acute disease or treatment effects supervene determinants of the underlying baseline ,23. Lab In order to study the effect of underlying health conditions we extracted ICD9 and ICD10 diagnosis codes from the EHR of the BioVU and MGI cohorts. We searched for diagnosis for 42 diseases with the potential to alter a clinical lab measurement . We starWe first performed GWAS analysis of each lab trait separately in the MGI and BioVU cohorts. We performed multiple GWAS for each lab, varying the statistic used to summarize the longitudinal lab measurements for each sample  and the inclusion of binary indicators for diagnosis comorbid diseases in the GWAS regression.epacts 3.3.0 [For each GWAS, the distribution of lab summary statistics was inverse normalized separately within the MGI and BioVU cohorts prior to regression analysis. In a separate analysis of the BioVU cohort, we determined that inverse normalization of lab values performed better than applying no transformation, or a log or square root transformation for controlling GWAS type I error. Genome-wide association tests were performed on the inverse normalized traits using additive linear regression models containing age, sex and four principal components as covariates. The comorbidity model controlled for disease status by inclusion of an additional 42 covariates for the binary disease phenotypes. The regression analyses were performed in the BioVU cohort using PLINK  and in tts 3.3.0 .p indicates a SNP that increases in significance  for the alternate summary statistic. A negative value of Δp indicates a decrease in significance for the alternate analysis. Scatterplots of Δp computed in MGI and BioVU summarize the magnitude and consistency of change in p-value significance between the cohorts  on a set of LD-pruned SNPs for each meta-analyzed lab.We meta-analyzed the GWAS results from the MGI and BioVU default analysis . The meta-analysis was performed using d errors . We compWe created a list of previously identified genetic associations for each analyzed lab trait using the GWAS catalog   or arithmetically-dependent traits , and therefore likely to indicate the same biological association. We report the “peak” or most significant SNP when a group of novel SNPs are in linkage disequilibrium.We performed a replication analysis of novel associations identified in the meta-analysis using an independent cohort of BioVU samples that became available after the original meta-analysis was performed. This replication cohort consisted of 29,043 European ancestry adult individuals with extant lab data recruited using the same procedure as the initial BioVU cohort, genotyped on the same MEGA genotyping array, and subjected to the same data QC procedure. We declared a novel association to be replicated if the replication p-value was <0.05 and the direction of effect was consistent with that from the meta-analysis.Data were collected according to Declaration of Helsinki principles. MGI study participants’ consent forms and protocols were reviewed and approved by the University of Michigan Medical School Institutional Review Board (IRB ID HUM00099605 and HUM00155849). Opt-in written informed consent was obtained for each MGI participant. BioVU is Vanderbilt University's biobank of DNA extracted from leftover and otherwise discarded clinical blood specimens. BioVU operates as a consented biorepository; all individuals must sign the BioVU consent form in order to donate future specimens.We extracted all available clinical lab measurements from the electronic health records (EHRs) for genotyped samples in two academic biobank cohorts: the Michigan Genomics Initiative  (MGI) atWe analyzed 70 labs matched with high confidence between the health systems and having at least 1,000 samples with the lab measured in each health system . We searλGC) confirmed the meta-analyses were well-controlled [λGC across all labs was 1.035, ranging between 0.995 and 1.103. Consistent with polygenicity [λGC values. The mean λGC for labs with zero testable catalog SNPs was 1.020. Labs with one to twenty testable Catalog SNPs had mean λGC of 1.028 and labs with greater than 20 testable Catalog SNPs had mean λGC of 1.066.The 70 EHR-derived lab traits were first analyzed separately in the cohorts using the mean measurement as the individual-level outcome. The meta-analysis sample size differed between labs, ranging from 7,429 for uric acid to 46,382 for hematocrit , reflectntrolled . The meagenicity , traits We first performed a replication analysis of the 1,313 GWAS catalog SNPs to validate the EHR-derived lab traits. Overall, we replicated 982 of the GWAS catalog SNPs, giving an overall replication rate of 74.8% . ReplicaSeveral factors influenced our ability to replicate individual catalog SNPs , each coReplication rate was also dependent on both the best previously reported p-value for the association and the sample size of the study reporting the association . Our repWe identified 264 SNP-lab trait pairs representing potentially novel associations. Based on visual inspection, these SNPs corresponded to 31 distinct peaks for which we report the lead SNP having the strongest association signal at each peak .We performed a replication analysis of the 31 lead SNPs using an independent cohort of 29,043 BioVU patients that became available after the initiation of our primary analysis. One SNP potentially novel for both immature granulocytes measures failed QC filtering in the replication cohort and could not be tested for replication. In total, we replicated 22 of the 31 (71%) novel associations . Among tTMPRSS6 (transmembrane serine protease 6), and rs8022180, an intronic variant in TRAF3, were shown to be associated with bilirubin and serum total protein level, respectively, in a Japanese population [GPT, a gene associated with alanine aminotransferase levels in the Korea Biobank [Several of our novel findings have biological or existing evidence that support the association. Three of the associations have recently been identified for the same lab in non-European cohorts. rs855791, a missense variant in pulation . rs11257 Biobank . Our resCTRB1 was associated with both amylase and lipase, clinical assays of pancreas function used to diagnose pancreatitis. While the SNP itself has previously been linked to blood protein measurements [CTRB1 gene encodes chymotrypsin, a component of digestive enzyme secreted by the pancreas, and was previously shown to be associated with alcoholic chronic pancreatitis [FUT9, a gene that showed association with diabetic neuropathy in a trans-ethnic meta-analysis [The intronic variant rs8051363 in urements , the CTRreatitis . A seconanalysis .AMY2B, AMY2A and AMY1) on chromosome 1, each of which encodes enzymes that digest starch into sugar [AMY2A, a pancreatic amylase enzyme, most common in populations historically lacking starch rich diets [The amylase-associated SNP rs1930212 resides near three amylase genes , a signaling protein involved in response to various hormones. Variation in GNAQ is associated with Sturge-Weber syndrome [One of our novel results for calcium, rs2839899, is an intronic variant in syndrome , a heredCOBLL1 previously linked to fasting blood insulin and Type 2 diabetes [NDUFAF6 and TP53INP1, has recent associations with T2D [Three SNPs showed associations with glucose. rs7607980 is a missense variant in diabetes –41. rs92diabetes . And, alwith T2D  and eosiwith T2D  among UKWe note that several associations occurred within the HLA region on chromosome 6, notably for glucose, hemoglobin A1C, and TSH. These variants are likely segregating with HLA types, which are strongly associated with various autoimmune diseases including diabetes and autoimmune thyroiditis, which have strong effects in these particular labs.We computed the genetic correlation between pairs of labs using LD score regression  to learnA prominent cluster of labs (top right corner of the heatmap) contains primarily white blood cell traits including measures of immature granulocytes, lymphocytes, monocytes and neutrophils. The immature granulocytes also showed a strong correlation with ferritin (ferrit), an iron storage and acute phase protein. Ferritin and immature granulocytes can both be elevated during severe acute inflammation, explaining this correlation.As expected, HgbA1C and glucose were strongly correlated. More interestingly, they also clustered with Red cell Distribution Width (RDW) and Erythrocyte Sedimentation Rate (SedRat). This cluster of labs showed negative associations with high density lipoprotein (HDL), mean cell hemoglobin concentration (MCHC), and mean cell hemoglobin (MCH). This supports a pathophysiology where the metabolic syndrome  is linked by complex mechanisms to persistent low-level inflammation (elevated SedRat), and anemia of chronic disease .We identified a cluster containing red cell indices–mean cell hemoglobin concentration (MCHC), mean cell hemoglobin (MCH), and mean cell volume (MCV)–with total bilirubin (Bili) and transferrin saturation (%SAT). This reflects the biology of hemoglobin–iron is carried to red cell precursors by transferrin and incorporated into heme and thence hemoglobin, red cells are filled with hemoglobin, and at the end of a red cell lifecycle, heme is broken down into bilirubin.Additional clusters include (1) calcium (Ca), albumin (Alb) and total protein in blood (TProt), (2) thyroid stimulating hormone (TSH) and lactate dehydrogenase (LDH), and (3) hematocrit (HCT), red blood cell count (RBC) and hemoglobin (Hgb) with free tetraiodothyronine (FT4). Albumin (Alb) is the major blood protein, so Alb levels are unsurprisingly correlated with total blood protein (TProt). Calcium homeostasis is driven by free calcium, while albumin acts as a calcium sink, therefore calcium (Ca) levels would reasonably be expected to correlate with Alb .Hematocrit (HCT), red blood cell count (RBC) and hemoglobin (Hgb) are interrelated measures of oxygen carrying capacity in blood and unsurprisingly correlated. In our study, they are also correlated with free tetraiodothyronine (FT4). Anemia  may be a feature of hypothyroidism (low FT4), and tetraiodothyronine—thyroid hormone—has been reported to play a role in red cell maturation ,48.A final cluster was identified linking thyroid stimulating hormone (TSH) to lactate dehydrogenase (LDH). Muscle breakdown, manifesting as weakness, is a feature of hypothyroidism, and therefore other laboratory anomalies seen in hypothyroidism include release of muscle enzymes including LDH ,49.We explored the impact of analytic choices on downstream analysis by performing parallel GWAS analyses in the MGI and BioVU cohorts with one of the analytic steps perturbed from our original analysis: either the individual-level statistic used to summarize longitudinal lab measurements  or the inclusion of covariates for underlying comorbid health conditions. We performed these analyses on the 22 lab traits for which there were least 20 testable GWAS catalog SNPs, using the catalog SNPs to interpret the effect of each analytic strategy on true risk variants.Overall, 13.3% of testable catalog SNPs showed a major change in significance when using the median as opposed to mean value for the summary statistic . The medIn comparison, the first available measurement and the maximum measurement had a greater impact on association p-values for catalog SNPs. In both cases, the alternate summary statistic was most likely to cause a concordant decrease in significance. Using the first available measurement resulted in concordant increase for only 3.1% of catalog SNPs, whereas 16.9% of catalog SNPs had a concordant decrease and 4.5% had discordant changes in significance. Using the maximum available measure had similar performance .Despite an overall trend of reducing significance of known risk variants, several related labs for blood oxygen carrying capacity did benefit from using the first available or maximum measurements. Red blood cell count (RBC), hematocrit (HCT) and hemoglobin (Hgb) each showed concordant increase in significance for several of their respective catalog SNPs without negatively impacting remaining catalog SNPs. This likely reflects red cell biology. Conditions that decrease oxygen carrying capacity, such as blood loss or iron deficiency are far more common than those that increase it, polycythemia vera or severe obstructive sleep apnea, for example. Thus, maximum measurement of an individual’s oxygen carrying capacity more likely represents the genetically determined set point.The comorbidity model, containing binary covariates for 42 comorbid diseases with the potential to alter lab values, produced the largest proportion of catalog SNPs (6.2%) with concordant increased significance in MGI and BioVU among the alternate analysis strategies considered . DespiteThe clearest example of a substantial and consistent effect on catalog SNPs between MGI and BioVU was for HDL and Mean platelet volume (MPV). Interestingly, in contrast to this result for HDL, LDL had no catalog SNPs with concordant increase in significance and seven catalog SNPs with concordant decrease.This study represents the first cross-health system study of EHR-derived lab traits at large scale. We performed meta-analysis GWAS of 70 lab traits and have made these association results easily accessible to the research community. Thoroughly dissecting each lab-SNP combination is a daunting task. Here, we focused on replication of GWAS catalog variants to validate our data and highlighted novel genetic associations. We anticipate that our full results, including those which do not reach genome-wide significance will be useful in replicating future novel results, in studies which synthesize findings across multiple SNPs, or in hypothesis-driven studies which require less stringent thresholds.Our study serves as a proof-of-principle for performing cross-health-system genetic analysis of EHR-derived lab traits. The high replication rate for known GWAS variants indicates that EHR lab traits can be well-matched between discordant health systems and that measurements taken during real-life medical interactions sufficiently reflect those taken under more idealized experimental conditions. Moreover, this implies that mechanisms underlying variation in lab traits among healthy populations also act in a health system population with diseased individuals, strengthening their clinical relevance. By comparing various analytic strategies, we show that there is no optimal strategy that holds across all lab traits. In fact, we observed many instances in which the alternate analysis simultaneously increased significance for some risk variants and decreased significance for others. Thus, even within a given lab trait, an optimal strategy for variant discovery might not exist. We also considered a summary statistic based on Area Under the Curve for the longitudinal lab data ,51. AnalThe primary strength of our study was the access to two independent biobank cohorts. Using two cohorts provides an increase in sample size and power over analyzing and reporting on each cohort separately. In addition, the two-cohort design adds a built-in internal consistency check to our results by requiring effect sizes to be in the same direction in both cohorts. This additional requirement reduced the potential for unknown biases in the health system cohorts to create spurious results when replicating GWAS catalog SNPs or novel association discovery. Further, the independent cohorts provided the means to rigorously examine the portability of analytic strategies between biobanks. A similar analysis performed in a single cohort could produce recommendations over fitted to one specific context. Use of multiple sites increases the generalizability of our recommendations. This study was further strengthened by the fortuitous availability of an independent tranche of BioVU samples that provided an immediate replication cohort for the novel findings of our meta-analysis.Our study has implications for the design and analysis of similar studies in the future. Matching and analyzing lab data between health systems is difficult and requires substantial content knowledge. This study benefited from a multi-disciplinary team consisting of clinical experts to lead the categorization of the raw lab data extracts and statistical geneticists to guide analytic strategies. We leaned heavily on GWAS catalog SNPs to serve as positive controls. We recommend researchers to incorporate an explicit replication step to validate lab data prior to testing novel hypotheses. Summarizing the longitudinal measurements simply using the mean proved relatively robust across labs but was by no means optimal in all scenarios. Future studies can benefit from considering a summary statistic suited to the specific lab trait being evaluated. Our analysis also highlights that close attention must be paid to differences in the preparation and analysis of EHR phenotypes, particularly longitudinal lab measurements. Failing to replicate a prior finding can be due to lack of a true effect but also a variety of differences between biobank cohorts and analytic procedures.We were motivated to examine the effect of controlling for disease status because of its use in the analysis of lab traits in BioBank Japan . ControlA limitation of studying clinical labs in real-life cohorts is that some measurements will be affected by medication. We were unable to formally address the effect of medication because of unreliable measurements of medication. However, it remains an important consideration for future EHR-based lab studies and requires further study. There was indication that in situations where a disease diagnosis is likely to be accompanied by medication, for example a diagnosis of dyslipidemia with lipid labs, controlling for disease status diagnosis serves as a reasonable proxy to treatment status. As research interest in EHR phenotypes increases, we anticipate that improved capture of prescription data will facilitate the identification of medication effects.A further limitation of this study is the number of analyzed genetic variants. The study was restricted to ~800K SNPs because BioVU imputed genotypes were unavailable at time of analysis. Although this limited our ability to discover novel variation, the number of SNPs was more than sufficient to perform the primary purpose of the paper, a proof-of-principle replication analysis across a broad range of clinical labs and analytic strategies. However, there are likely many loci remaining to be discovered for these labs, particularly the understudied traits.In conclusion, we report the first lab-wide genome-wide association study linking data between two independent EHR-based cohorts. We achieved a high degree of replication of prior associations and report a modest number of new associations. In melding these data sets, we addressed key questions in design and analysis of ‘real world’ data that are increasingly relevant.S1 Table(XLSX)Click here for additional data file.S2 TableThese associations have been reported at least once in a mixed-sex, adult, European-predominant population not selected for the presence of any disease.(XLSX)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S1 FigPlease refer to the Methods section for a complete description. The x-axis corresponds the fold changes for the SNP in MGI and the y-axis corresponds to the fold changes for BioVU. Positive log-fold changes indicate that the alternative statistic yielded a smaller (more significant) p-value than using the mean as a summary statistic. The upper-right (green) quadrant plots SNPs that decreased in p-value in both cohorts for the alternative statistic. The lower-left (blue) quadrant plots SNPs that increased in p-value in both cohorts. The two remaining quadrants indicate SNPs with discordant changes in p-value between the cohorts. GWAS catalog SNPs are plotted in red, novel SNPs for a given lab (if applicable) are plotted in purple, and the remaining SNPs are LD-pruned (for plotting convenience) and plotted in black. The white diamond displays an empirical null distribution of fold changes for non-associated SNPs. The first 22 pages display the three alternative summary statistics  for a single lab. The following six pages contain the analogous plots showing log fold change in p-values for the comorbidity model, which includes binary covariates for various comorbid diseases with the potential to impact lab measures, to a default analysis that does not account for comorbidities.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.


These formulae clearly show that a high heritability does not imply an accurate prediction when p > n. Both the estimation of heritability via GCV and the prediction accuracy formulae are validated using simulated data and real data from UK Biobank.Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts for the many small contributions of SNPs in the explanation of a phenotype. This paper approaches heritability from a machine learning perspective, and examines the close link between mixed models and ridge regression. Our contribution is two-fold. First, we propose estimating genomic heritability using a predictive approach via ridge regression and Generalized Cross Validation (GCV). We show that this is consistent with classical mixed model based estimation. Second, we derive simple formulae that express prediction accuracy as a function of the ratio  The old nature vs. nurture debate is about whether a complex human trait is determined by a person's genes or by the environment. It is a longstanding philosophical question that has been reinvestigated in the light of statistical genetics , defined as the proportion of phenotypic variance explained by the additive effects of variants.There are two definitions of heritability in common use: first, there is Hg is the sum of independent genetic terms, and with e assumed to be normal. This heritability in the narrow sense is a function of the correlation between the phenotypes of relatives.The quantification of narrow-sense heritability goes back to family studies by Fisher , who intAlthough Fisher's original model makes use of pedigrees for parameter estimation, some geneticists have proposed using the same model with genetic data from unrelated individuals . Unfortunately, this is complicated in practice for three reasons: the causal variants are not usually available among genotyped variants; genotyped variants are in linkage disequilibrium (LD); and the least squares estimate is only defined when n > p, which is not often the case in a GWAS  of the ridge regression estimator (variance plus the squared bias) is smaller than the MSE of the Maximum Likelihood estimator  and minimizing a ridge criterion  derived from the mixed model and ridge regression . Estimating the variance components of the mixed model may be interpreted as a kind of empirical Bayes approach, where the ratio of the variances is the regularization parameter that is usually estimated using a single dataset. In contrast to this, in order to properly estimate the ridge regression regularization hyperparameter that gives the best prediction, two datasets are required. If a single dataset were to be used, this would result in an insufficiently regularized  model offering too high prediction performances on the present dataset but unable to predict new samples well. This over-fitting phenomenon is particularly evident when dimensionality is high.R) tends to the null vector. Conversely, when λ tends to zero, the model approaches maximum complexity. One solution for choosing the right complexity is therefore to use both a training set to estimate the effect vector for different values of the hyperparameter and a validation set to choose the hyperparameter value with the best prediction capacity on this independent sample. An alternative solution, when data is sparse, is to use a cross-validation approach to mimic a two-set situation. Finally, it should be noted that the estimation of prediction performance on a validation dataset is still overoptimistic, and consequently a third dataset, known as a test set, is required to assess the real performance of the model.The fact that the complexity of the ridge model is controlled by its hyperparameter can be intuitively understood when considering extreme situations. When λ tends to infinity, the estimated effect vector  to speed up the estimation of the hyperparameter λ and thus to estimate the additive heritability A classical strategy for choosing the ridge regression hyperparameter uses a grid search and n ridge regression estimations (where n is the number of observations) at each grid value, but involves a single run. It thus provides a much faster and convenient alternative for choosing the hyperparameter. We haveThe GCV error in Equation (9)  of the p > n), the use of GCV after empirical centering of the data can lead to a strong bias in the choice of λ and thus in heritability estimation. Let us illustrate the problem with a simple simulation. We simulate a phenotype from synthetic genotype data with a known heritability of h2 = 0.25, n = 1, 000 individuals, p = 10, 000 variants and 100% causal variants. The simulation follows the additive polygenic model without intercept or covariates, as described in section 2.3. Before applying GCV, genotypes are standardized in the most naive way: the genotype matrix M is empirically centered and scaled column-wise, resulting in the matrix Z. Since we want to mimic an analysis on real data, let us assume that there is a potential intercept in our model :In high dimensional settings .M and of the phenotype. By centering the columns of M with the empirical means of those columns, a dependency is introduced, and each line of the resulting standardized genotype matrix Z becomes a linear combination of all the others. The same phenomenon of dependency can be observed with the phenotype when using empirical standardization. Given the nature of the LOO in general , this kind of standardization introduces a link between the validation set and the training set at each step: the “validation set individual” can be written as a linear combination of the individuals in the training set. In high dimension, this dependency leads to This is a direct consequence of the empirical standardization of ZZT has at least one null eigenvalue and an associated constant eigenvector in a high dimensional setting  = 0n−r−1 provides a solution (see section 6 in C is therefore necessary.Contrast matrices are a commonly used approach in the field of mixed models for REstricted Maximum Likelihood computations (REML)  and for the estimation of the genetic effects. This can be overcome by partitioning our data. Splitting our data into a standardization set and a training set, we will first use the standardization set to estimate the mean and the standard deviation of each variant, the intercept, and the potential covariate effects. Those estimators will then be used to standardize the training set on which GCV can then be applied.This method has two main drawbacks. The first is that the estimation of the non-penalized effects is done independently of the estimation of the genetic effects, even though in practice we do not expect covariates to be highly correlated with variants. The other drawback is that it reduces the number of individuals for the heritability estimation . This approach therefore requires a larger sample than when using projection.Ridge regression helps to highlight the link between heritability and prediction accuracy. What is the relation between the two concepts? Is prediction accuracy an increasing function of heritability?In a machine learning setting, we have training and testing sets. The classical bias-variance trade-off formulation considers the expectation of the loss over both the training set and the test individual phenotype. It breaks down the prediction error into three terms commonly called variance, bias, and irreducible error. In this paper we do consider the genotypes of the training set as fixed and the genotype of a test individual as random, and somewhat abusively continue to employ the terms variance, bias, and irreducible error:tr refers to the training set, while te refers to the test set.where the index tr to lighten notations) whose columns have zero mean and unit variances, we denote zte[zte] = 0p and var(zte) = Ip, irreducible error, variance, and bias become:Assuming a training set genotype matrix u is the vector of the ridge parameters.where ZZT and ZTZ are the empirical estimations of covariance matrices of respectively the individuals and the variants . Two separate situations can be distinguished according to the n/p ratio. In the high-dimensional case where p > n, the matrix ZZT estimates well the individuals' covariance matrix up to a factor p. Where n > p, on the other hand, ZTZ estimates well the covariance matrix of variants up to a factor n. Eventually, n < p and n > p.Since individuals are assumed to be unrelated, the covariance matrix of the individuals is diagonal. The covariance matrix of the variants is also diagonal, since variants are assumed independent. Assuming scaled data, Assuming further thati ∈ ] var(yi) = 1, we then have σ2 = 1 − h2,∀heritability is equally distributed among normalized variants i.e., Zu)T(Zu) ≃ nh2,the expected prediction error can be stated more simply, according to the n becomes much larger than p. What is more surprising is that the prediction error is close to the maximum, whatever the heritability, when n is much smaller than p. Paradoxically, even with the highest possible heritability, if the number of variants is too large in relation to the number of individuals, no prediction is possible. This can be explained by the fact that the penalization plays a very important role in that case and thus strongly increases the bias, while reducing the variance. The squared bias and the variance with respect to the log ratio of the number of individuals over the number of variants in the training set are shown in When considering the theoretical quadratic error with respect to the log ratio of the number of individuals over the number of variants in the training set , as expeSimilarly, the prediction error can be computed on the training set instead of on the test set. Using the same assumptions as before, the expected prediction error on the training set can be approximated by:p > n, the error on the training set does not depend on the n/p ratio. When n becomes greater than p, it increases and tends toward the irreducible error 1 − h2 when n ≫ p. As shown in n/p ratio, which is linear when p > n and tends toward zero when n ≫ p.A graph similar to yte and te  from the UK Biobank dataset.We first assess the performance of GCV for heritability estimation and then look at the accuracy of the prediction when the ratio of the number of individuals to the number of variants varies in the training set.p-values of Hardy-Weinberg equilibrium test > 1e-7; Minor Allele Frequency > 0.01), leading to 473,054 and 417,106 remaining individuals and SNPs, respectively.The present analyses were conducted under UK Biobank data application number 45,408. The UK Biobank dataset consists of ≃ 784 K autosomal SNPs describing ≃ 488 K individuals. We applied relatively stringent quality control and minor allele frequency filters to the dataset  and BMI. In order to have a homogeneous population for the analysis of these real phenotypes, we retained only those individuals who had reported their ethnicity as white British and whose Principal Component Analysis (PCA) results obtained by UK Biobank were consistent with their self-declared ethnicity. In addition, each time we subsampled individuals we removed related individuals [one individual in all pairs with a Genetic Relatedness Matrix (GRM) coefficient >0.025 was removed], as in Yang et al.  in orderp variants, we first set a vector of variant frequencies f ∈ ℝp, with these frequencies independently following a uniform distribution f, to form the genotype matrix M. A matrix of standardized genotypes Z* can be obtained by standardizing M with the true variant frequencies f.The synthetic genotype matrices are simulated as in Golan et al.  and de VWe consider both synthetic and real genetic data, and simulate associated phenotypes.n the number of individuals and p the number of variants), the fraction of variants with causal effects fc, and the true heritability In the two simulation scenarios we investigate the influence on heritability estimation of the following three parameters: the shape of the genotype matrix in the training set . To create the vector of genotype effects u, p × fc causal SNPs are randomly sampled and their effects are sampled from a multivariate normal distribution with zero mean and a covariance matrix τIp×fc (where p×(1 − fc) effects are set to 0. The vector of environmental effects e is sampled from a multivariate normal distribution with zero mean and a covariance matrix y = Z*u + e and y = Zu + e, for the fully synthetic scenario and the semi-synthetic scenario, respectively. A standardization set of 1,000 individuals (that will be used for the GCV approach based on two datasets) is also generated for each scenario in the same way.For each simulation scenario and for a given a set of parameters  genotype matrix for the training set in the fully synthetic scenario and used this simulated matrix for all the 9 × 3 × 3 = 81  genotype matrix for the training set in the semi-synthetic scenario. Smaller matrices were then created from a subset of these two large matrices . Consequently, computation of the GRM and its eigen decomposition needed to be performed only once for each n/p ratio considered. The fully synthetic and the semi-synthetic scenarios were each replicated 30 times.Applying GCV to large-scale matrices can be extremely time-consuming, since it requires the computation of the GRM associated with  Groenen  in ordern = 1,000, and a test set of size nte = 5,000. The maximum number of variants was set to pmax = 50,000 and the heritability to h2 = 0.6. We first simulated a global allelic frequency vector We performed fully synthetic simulations for different ratios p < pmax, we selected a vector of genetic effects composed of the p first components of u multiplied by a Mte was then simulated and its normalized version p components of f. The noise vector For each subset of variants of size k. A prediction up obtained with yte, k was also estimated.We generated 300 training sets by simulating the normalized genotype matrix, noise, and phenotype using the same process as for the test set. Here, the training set index is denoted as We considered the following numbers of variants:To experiment on UK Biobank for assessing the prediction accuracy, for each phenotype we considered three sets of data: a training set for the purpose of learning genetic effects, a standardization set for learning non-penalized effects (covariates and intercept), and a test set for assessing predictive power. Pre-treatment filters (as described in section 2.3.1) were systematically applied on the training set. We computed the estimation of genetic effects using the projection-based approach to take into account non-penalized effects, where the penalty parameter was obtained by GCV with the same projection approach:X contains the intercept):We then estimated non-penalized effects  gives heritability estimations that are very similar to those obtained using the GCV approaches. The value of simulated heritability does not appear to have a strong effect on the quality of the heritability estimation. On the other hand, the ratio n/p seems to have a real impact on estimation variance, with lower ratios leading to lower variances, which initially might appear surprising. One possible explanation for this is that in our simulations n increases as the ratio n/p decreases. Visscher and Goddard . All three approaches take account of covariates and the intercept. The AI-REML approach also uses a projection matrix to deal with covariates. For the GCV approach based on two datasets, a standardization set of 1,000 individuals is also sampled, and for comparison purposes we have chosen to apply this two-set strategy to the classical mixed model approach as well.Since the true heritability is of course unknown with real data, the sampling of the training and standardization sets is repeated 10 times in order to account for heritability estimation variability. Note that the SNP quality control and MAF filters were repeated at each training set sampling and applied to the standardization set.n > p, the mean of the test set is close to the minimum possible error, which means that the ridge regression provides a reliable prediction on average.As expected, the mean of the test set error follows closely the theoretical curve when the p varies . When n Interestingly, if the mean error behaves as expected by our approximation, the standard deviation of the error may be very large. n ≫ p, that is to say when the genetic term is correctly estimated, irrespective of the training set.The error bars in n ≫ p, the squared correlation tends toward the simulated heritability. We compared our approximation with the approximation obtained by Daetwyler et al. .Let us consider the proposed theoretical approximation of the predictive power of ridge regression with respect to the The two phenotypes differ considerably as regards heritability: we estimate by the projection-based GCV approach that 73.33% of height is “heritable” whereas only 33.91% of BMI is .These estimated values are close to those currently found in the literature . We estimate the effective n value using the observed mean square errors, the estimated heritability, and the theoretical relation in the case of independent variants p > n. We then use a simple linear regression to find the coefficient between these estimated A major difference between UK Biobank data and our simulations designed to check the proposed approximation lies in the strong linkage disequilibrium present in the human genome. Several papers have proposed using the effective number of independent markers to make adjustments in the multiple testing framework . Using this error normalization and adjusting the theoretical curve for an effective number of SNPs, we observe a close fit between the estimated errors on the test set and their theoretical values .In this work we investigated an alternative computation of genomic heritability based on ridge regression. We proposed a fast, reliable way to estimate the optimal penalization parameter of the ridge via Generalized Cross Validation adapted for high dimension. The genomic heritability estimated from the GCV gives results comparable to mixed model AIREML estimates. It clearly demonstrates that a predictive criterion allows a reliable choice of the penalization parameter and associated heritability, even when the prediction accuracy of ridge regression is low. Moreover, even though our approach does not formally consider Linkage Disequilibrium, simulations showed that it still provides reliable genomic heritability estimates in presence of realistic Linkage Disequilibrium.p > n and to Rabier et al. . It would show whether it is possible for the prediction accuracy to exceed the upper bound proposed by De los Campos et al. . The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.EL and CA designed and directed the study. AF, EL, and CA wrote the manuscript. AF created the synthetic datasets. AF performed all the analyses with substantial input from EL, CA, CD-R, and MP-J. All authors discussed the results and commented on the manuscript.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.