Biology subjects

Gieger, C.

Publications and source records attributed to Gieger, C..

At least 19 recordsLinked to original sources

bioRxiv2025

Epigenome-wide association study meta-analysis of wellbeing

Wellbeing is associated with both behavioral phenotypes as well as several key life outcomes, such as health, employment, and coping with stressful events. These phenotypes associated with wellbeing could be potential indicators of differential epigenetic patterns between individuals that differ in their levels of wellbeing. We performed the largest epigenome-wide (EWAS) meta-analysis of wellbeing to date by combining whole blood DNA methylation data (Illumina 450k array) from 13 cohorts from Europe, Australia, and the USA (N = 10,757 participants). After correcting for smoking and BMI, no epigenome-wide significant methylation sites were identified. We tested whether a weighted methylation score (MS) based on leave-one-cohort-out EWAS meta-analysis summary statistics predicted wellbeing in an independent cohort, and whether prediction was significant over and above the polygenic score (PGS) for wellbeing. The MS was associated with wellbeing (variance explained=0.22%, p=0.03) and was no longer significant after adding the polygenic score (PGS; variance explained=0.43%, p=0.0046, MS; variance explained=0.07%, p=0.2842). We further compared DNA methylation levels in 16 pairs of monozygotic twins discordant for wellbeing. These analyses revealed no significant within-pair DNA methylation differences at the top-sites from the meta-analysis or in MS. Our results suggest that larger EWAS meta-analyses with uniform phenotype assessment are required to identify methylation sites associated with wellbeing.

genomics↗

bioRxiv2025

Theobromine is Associated with Slower Epigenetic Ageing

Theobromine, a commonly consumed dietary alkaloid derived from cocoa, has been linked to extended lifespan in model organisms and to health benefits in humans. We examined associations between circulating theobromine intake, measured using serum metabolomics, and blood-based epigenetic markers of biological ageing in two European human population-based cohorts. Serum theobromine levels were significantly associated with reduced epigenetic age acceleration, as measured by GrimAge (p<2e-7) and DNAmTL (p<0.001) in over 500 individuals from the TwinsUK cohort, and both signals replicated in 1,160 individuals from the KORA cohort (p = 7.2e-08 and p = 0.007, respectively). Sensitivity analyses including covariates of other cocoa and coffee metabolites suggest that the effect is specific to theobromine. Our findings indicate that the reported beneficial links between theobromine intake on health and ageing extend to the molecular epigenetic level in humans.

genetics↗

bioRxiv2024

The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy

Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1,091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5{+/-}0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved {Delta}AUROC >0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases.

bioinformatics↗

bioRxiv2023

Missing view completion for multi-timepoints omics data via representation disentanglement and temporal knowledge transfer

Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on three benchmark datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.

bioinformatics↗

bioRxiv2020

Genome-wide association studies identify 137 loci for DNA methylation biomarkers of ageing

Biological ageing estimators derived from DNA methylation (DNAm) data are heritable and correlate with morbidity and mortality. Leveraging DNAm and SNP data from >41,000 individuals, we identify 137 genome-wide significant loci (113 novel) from meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We report strong genetic correlations with longevity and lifestyle factors such as smoking, education, and obesity. Significant associations are observed in polygenic risk score analysis and to a lesser extent in Mendelian randomization analyses. This study illuminates the genetic architecture underlying epigenetic ageing and its shared genetic contributions with lifestyle factors and longevity.

genetics↗

bioRxiv2020

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BackgroundMolecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans -QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information has been proposed to alleviate network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans -QTL hotspots using human cohort data and data-driven prior information. ResultsWe devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were used to recover the regulatory networks underlying trans -QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. ConclusionWe demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms.

bioinformatics↗

bioRxiv2019

Genome-wide association study identifies 49 common genetic variants associated with handedness.

Handedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the worlds largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 41 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (P < 5x10-8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8% - 6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2% - 17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 - 0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

genetics↗

bioRxiv2019

A genome-wide genetic pleiotropy approach identified shared loci between multiple system atrophy and inflammatory bowel disease

We aimed to identify shared genetic background between multiple system atrophy (MSA) and autoimmune diseases by using the conjFDR approach. Our study showed significant genetic overlap between MSA and inflammatory bowel disease and identified DENND1B, C7, and RSP04 loci, which are linked to significant changes in methylation or expression levels of adjacent genes. We obtained evidence of enriched heritability involving immune/digestive categories. Finally, an MSA mouse model showed dysregulation of the C7 gene in the degenerating midbrain compared to wildtype mice. The results identify novel molecular mechanisms and implicate immune and gut dysfunction in MSA pathophysiology.

genetics↗

bioRxiv2019

Global variability of the human IgG glycome

Immunoglobulin G (IgG) is the most abundant serum antibody and is a key determinant of the humoral immune response. Its structural characteristics and effector functions are modulated through the attachment of various sugar moieties called glycans. IgG N-glycome patterns change with the age of individual and in different diseases. Variability of IgG glycosylation within a population is well studied and is affected by a combination of genetic and environmental factors. However, global inter-population differences in IgG glycosylation have never been properly addressed. Here we present population-specific N-glycosylation patterns of whole IgG, analysed in 5 different populations totalling 10,482 IgG glycomes, and of IgGs fragment crystallisable region (Fc), analysed in 2,530 samples from 27 populations sampled across the world. We observed that country of residence associates with many N-glycan features and is a strong predictor of monogalactosylation variability. IgG galactosylation also strongly correlated with the development level of a country, defined by United Nations health and socioeconomic development indicators. We found that subjects from developing countries had low IgG galactosylation levels, characteristic for inflammation and ageing. Our results suggest that citizens of developing countries may be exposed to country-specific environmental factors that can cause low-grade chronic inflammation and the apparent increase in biological age.

biochemistry↗

bioRxiv2019

Cis-epistasis at the LPA locus and risk of coronary artery disease

Identification of epistasis affecting complex human traits has been challenging. Focusing on known coronary artery disease (CAD) risk loci, we explore pairwise statistical interactions between 8,068 SNPs from ten CAD genome-wide association studies (n=30,180). We discovered rs1800769 and rs9458001 in the vicinity of the LPA locus to interact in modulating CAD risk (P=1.75x10-13). Specific genotypes (e.g., rs1800769 CT) displayed either significantly decreased or increased risk for CAD in the context of genotypes of the respective other SNP (e.g., rs9458001 GG vs. AA). In the UK Biobank (n=450,112) significant interaction of this SNP pair was replicated for CAD (P=3.09x10-22), and was also found for aortic valve stenosis (P=6.95x10-7) and peripheral arterial disease (P=2.32x10-4). Identical interaction patterns affected circulating lipoprotein(a) (n=5,953; P=8.7x10-32) and hepatic apolipoprotein(a) (apo(a)) expression (n=522, P=2.6x10-11). We further interrogated potential biological implications of the variants and propose a mechanism explaining epistasis that ultimately may translate to substantial cardiovascular risks.

genetics↗

bioRxiv2018

Epigenetic derepression of FKBP5 by aging and stress contributes to NF-kB-driven inflammation and cardiovascular risk

Aging and psychosocial stress are associated with increased inflammation and disease risk, but the underlying molecular mechanisms are poorly understood. Because both aging and stress are also associated with lasting epigenetic changes, a plausible hypothesis is that stress exposure along the lifespan could confer disease risk by epigenetically deregulating molecules involved in inflammatory processes. Here, by combining large-scale analyses in human cohorts with mechanistic in vitro investigations, we found that FKBP5, a protein implicated in stress physiology, contributes to these relations. Across independent human cohorts (total n=3,131), aging and stress-related phenotypes were synergistically associated with epigenetic derepression of FKBP5. These age/stress-related epigenetic effects were recapitulated in an in vitro model of replicative senescence, whereby we exposed replicating human fibroblasts to stress (glucocorticoid) hormones. Unbiased genome-wide analyses in human blood linked higher FKBP5 mRNA with a proinflammatory profile and altered NF-{kappa}B-related gene networks. Accordingly, experiments in immune cells showed that FKBP5 overexpression promotes inflammation by strengthening the interactions of NF-{kappa}B regulatory kinases, whereas opposing FKBP5 either by genetic deletion (CRISPR/Cas9-mediated) or selective pharmacological inhibition prevented the effects on NF-{kappa}B. Further, the age/stress-related epigenetic signature enhanced FKBP5 responsivity to NF-{kappa}B through a positive feedback loop and was present in individuals with a history of acute myocardial infarction, a disease state linked to peripheral inflammation. These findings suggest that FKBP5-NF-{kappa}B signaling mediates inflammation associated with aging and stress, potentially contributing to cardiovascular risk, and may thus point to novel biomarker and treatment possibilities.\n\nSignificanceDiseases of the aging are the leading cause of morbidity and mortality. Elucidating the molecular mechanisms through which modifiable factors, such as psychosocial stress, confer risk for aging-related disease can have profound implications. Here, by combining studies in humans with experiments in cells, we find that aging and stress synergize to epigenetically derepress FKBP5, a protein implicated in stress physiology. Higher FKBP5 promotes inflammation by activating the master immune regulator NF-{kappa}B, whereas opposing FKBP5 - either genetically or pharmacologically- prevents the effects on NF-{kappa}B. Further, the age/stress-related epigenetic signature of FKBP5 is associated with history of myocardial infarction, a disease state linked to inflammation. These findings provide molecular insights into stress-related disease and may point to novel biomarker and treatment possibilities.

genomics↗

bioRxiv2018

Alcohol consumption is associated with widespread changes in blood DNA methylation: analysis of cross-sectional and longitudinal data

BackgroundDNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart.\n\nMethodsUsing the Illumina Infinium HumanMethylation450 BeadChip, DNA methylation measures were determined using baseline peripheral blood samples from 5,606 adult Melbourne Collaborative Cohort Study (MCCS) participants. For a subset of 1,088 of them, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models adjusted for batch effects and potential confounders. Independent data from the LOLIPOP (N=4,042) and KORA (N=1,662) cohorts were used to replicate associations discovered in the MCCS.\n\nResultsCross-sectional analyses identified 1,414 CpGs associated with alcohol intake at P<10-7, 1,243 of which had not been reported previously. Of these 1,243 novel associations, 1,078 were replicated (P<0.05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated (P<0.05) 403 of 518 associations that had been reported previously. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1,414 CpGs, 530 were differentially methylated (P<0.05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1,414 cross-sectional associations.\n\nConclusionOur study indicates that, for middle-aged and older adults, alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with changes in alcohol consumption.

epidemiology↗

bioRxiv2018

Defining the genetic control of human blood plasma N-glycome using genome-wide association study

Glycosylation is a common post-translational modification of proteins. It is known, that glycans are directly involved in the pathophysiology of every major disease. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here, we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people. We discovered and replicated twelve loci. This allowed us to demonstrate a clear overlap in genetic control between total plasma and IgG glycosylation. Majority of loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3, and MGAT5). We, however, also found loci that are likely to reflect other, more complex, aspects of plasma glycosylation process. Functional genomic annotation suggested the role of DERL3, which potentially highlights the role of glycoprotein degradation pathway, and such transcription factor as IKZF1.

genetics↗

bioRxiv2018

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

BACKGROUNDUntargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in epidemiological studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.\n\nMETHODSWe investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established genetically metabolic quantitative trait loci.\n\nRESULTSRun day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations (MICE) performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.\n\nCONCLUSIONMissing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.\n\nKey messagesO_LIUntargeted MS-based metabolomics data show missing values due to both batch-specific LOD-based and non-LOD-based effects.\nC_LIO_LIStatistical evaluation of multiple imputation methods was conducted on both simulated and real datasets.\nC_LIO_LIBiological evaluation on real data assessed the ability of imputation methods to preserve statistical inference of biochemical pathways and correctly estimate effects of genetic variants on metabolite levels.\nC_LIO_LIKNN-based imputation on observations with variable pre-selection and K = 10 showed robust performance for all data scenarios across all evaluation schemes.\nC_LI

systems biology↗

bioRxiv2018

Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of {small tilde}27M variants ({small tilde}21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5x10-8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10-5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%[≤]MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

genomics↗

bioRxiv2017

An exact and efficient score test for variance components models

Testing for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of p-values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 (n=13,950) study, and in particular when the individuals in the sample are unrelated. In these cases the SKAT approximation tends to be highly over-conservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact p-values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT.

genetics↗

bioRxiv2017

Genome-wide Association Study Of Plasma Proteins Identifies Putatively Causal Genes, Proteins, And Pathways For Cardiovascular Disease

Identifying genetic variants associated with circulating protein concentrations (pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteomes causal role in disease and bridge a GWAS knowledge gap for hitherto unexplained SNP-disease associations. We conducted GWAS of 71 high-value proteins for cardiovascular disease in 6,861 Framingham Heart Study participants followed by external replication. We comprehensively mapped thousands of pQTLs, including functional annotations and clinical-trait associations, and created an integrated plasma-protein-QTL searchable database. We next identified 15 proteins with pQTLs coinciding with coronary heart disease (CHD)-related variants from GWAS or tested causal for CHD by Mendelian randomization; most of these proteins were associated with new-onset cardiovascular disease events in Framingham participants with long-term follow-up. Identifying pQTLs and integrating them with GWAS results yields insights into genes, proteins, and pathways that may be causally associated with disease and can serve as therapeutic targets for treatment and prevention.

epidemiology↗

bioRxiv2016

Network based conditional genome wide association analysis of human metabolomics

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ( omics) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.\n\nResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.\n\nConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific contra-intuitive pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.\n\nShort abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

genomics↗