Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Pinu, Farhana R.; Beale, David J.; Paten, Amy M.; Kouremenos, Konstantinos A.; Swarup, Sanjay; Schirra, Horst Joachim; Wishart, David S.

doi:10.3390/metabo9040076

Cited by 481 publications

(352 citation statements)

References 170 publications

Supporting

Mentioning

349

Contrasting

Unclassified

Order By: Relevance

“…We first conducted simulations to assess the predictive capability and power to detect gene-trait associations under various phenotype heritability (h 2 p ), local heritability of expression (h 2 e,l ), distal heritability of expression (h 2 e,d ), and proportion of causal local (p c,l ) and distal (p c,e ) SNPs for MeTWAS and DePMA. We considered two scenarios for each combination of (h 2 p , h 2 e,l , h 2 e,d , p c,l , p c,e ): (1) the leveraged association between the distal-SNP and gene of interest exists in both the reference and imputation panel, and (2) the leveraged association between distal-SNP and gene of interest exists in the reference panel but is null in the imputation panel.…”

Section: Simulation Frameworkmentioning

confidence: 99%

“…Genomic methods that borrow information from multiple data sources, or "omics" assays, offer advantages in interpretability, statistical efficiency, and opportunities to understand the flow of information in disease regulation [1,2]. Transcriptome-wide association studies (TWAS) aggregate genetic information into functionally-relevant testing units that map to genes and their expression in a relevant tissue.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Bhattacharya

Love

2020

Preprint

View full text Add to dashboard Cite

Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or possible effects underlying the SNP-gene association. Here, we outline multi-omic strategies for transcriptome imputation from germline genetics for testing gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final model as fixed effects with local SNPs to the gene included as regularized effects. In the second extension, we assess distal-eSNPs (SNPs in eQTLs) for their mediation effect through mediators local to these distal-eSNPs. Highly mediated distal-eSNPs are then included in the eventual transcriptomic prediction model. We show considerable gains in percent variance explained of gene expression and TWAS power to detect gene-trait associations using simulation analysis and real data applications with TCGA breast cancer data and in ROS/MAP brain tissue data. This integrative approach to transcriptome-wide imputation and association studies aids in understanding the complex interactions underlying genetic regulation within a tissue and identifying important risk genes for various traits and disorders.the matrix X Mj be the local-genotype dosages in a 500 kilobase window around mediator j, 1 ≤ j ≤ m G . Furthermore, let M j be the intensity of mediator j (methylation M -value if j is a CpG site, expression if j is a miRNA or a gene, etc). Prior to any modeling, we scale Y G and all M j , 1 ≤ j ≤ m G to zero mean and unit variance. We also residualize M j , 1 ≤ j ≤ m G and Y G with the covariate matrix X C to account for population stratification using principal components of the global genotype matrix and relevant clinical covariates to obtainM j , 1 ≤ j ≤ m G andỸ G .Transcriptome prediction in MeTWAS draws from two-step regression, as summarized in Figure 1.First, in the training set for a given training-test split, for 1 ≤ j ≤ m G , we model the residualized intensitỹ M j of training-set specific mediator j with the following additive model:where w j is the effect-sizes of the SNPs in X † Mj onM j in the training set. As in traditional transcriptomic imputation models [3,4], we findŵ j using one of the two following methods with the largest predicted adjusted R 2 : (1) elastic net regression with mixing parameter α = 0.5 and λ tuned over 5-fold cross validation using glmnet [19], or (2) linear mixed modeling assuming random effects for X Mj using rrBLUP [20]. Only significantly heritable (default P < 0.05 for the likelihood ratio test) [21] and well-cross validated (default R 2 ≥ 0.01) expression models are considered.For all j, using these opti...

show abstract

Section: Simulation Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Bhattacharya

Love

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Metabolites are the functional endpoint of biological processes, which do not act alone and are regulated by upstream regulators at a given physiological condition [26,27]. PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase alpha), which are part of regulatory networks.…”

Section: Biological Significance Of Annotated Metabolitesmentioning

confidence: 99%

Plasma metabolomics of presymptomaticPSEN1-H163Y mutation carriers: A pilot study

Natarajan

Ullgren

Khoshnood

et al. 2020

Preprint

View full text Add to dashboard Cite

Background and Objective:PSEN1-H163Y carriers, at the presymptomatic stage, have reduced 18 FDG-PET binding in the cerebrum of the brain [1]. This could imply dysfunctional energy metabolism in the brain. In this study, plasma of presymptomatic PSEN1 mutation carriers was analyzed to understand associated metabolic changes. Methods:We analyzed plasma from non-carriers (NC, n=8) and presymptomatic PSEN1-H163Y mutation carriers (MC, n=6) via untargeted metabolomics using gas and liquid chromatography coupled with mass spectrometry, which identified 1199 metabolites. All the metabolites were compared between MC and NC using univariate analysis, as well as correlated with the ratio of Aβ1-42/Aβ1-40, using Spearman's correlation. Altered metabolites were subjected to Ingenuity Pathways Analysis (IPA). Results:When comparing between presymptomatic MC and NC, the levels of 116 different metabolites were altered. Out of 116, only 23 were annotated metabolites, which include amino acids, fatty acyls, bile acids, hexoses, purine nucleosides, carboxylic acids, and glycerophosphatidylcholine species. 1-docosapentaenoyl-GPC, glucose and uric acid were correlated with the ratio of plasma Aβ1-42/Aβ1-40 (p < 0.05). Conclusion:This study finds dysregulated metabolite classes, which are changed before the disease onset. Also, it provides an opportunity to compare with sporadic Alzheimer's Disease.Observed findings in this study need to be validated in a larger and independent Familial Alzheimer's Disease (FAD) cohort.

show abstract

“…The number of bi-or multi-omics data sets increase, yet combined analysis is not yet well explored. Several reviews (Pinu et al 2019;Hasin, Seldin, and Lusis 2017;Huang, Chaudhary, and Garmire 2017) discuss the potentials of multi-omics data algorithms directly pointing out the methodological gap of identifying and analyzing cross omic relations. One exception is the field quantitative trait (QT) analysis (Heinig et al 2017;Zhernakova et al 2017) .…”

Section: Introductionmentioning

confidence: 99%

Knowledge guided multi-level network inference

Ogris

Arloth

et al. 2020

Preprint

View full text Add to dashboard Cite

Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of so-called multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Few exceptions exist, for example the pairwise integration for quantitative trait analysis. However, the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. Here we propose a versatile approach, to perform a multi-level integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo. KiMONo performs network inference using statistical modeling on top of a powerful knowledge-guided strategy exploiting prior information from biological sources. Within the resulting network, nodes represent features of all input types and edges refer to associations between them, e.g. underlying a disease. Our method infers the network by combining sparse grouped-LASSO regression with a genomic position-confined Biogrid protein-protein interaction prior. In a comprehensive evaluation, we demonstrate that our method is robust to noise and still performs on low-sample size data. Applied to the five-level data set of the publicly available Pan-cancer collection, KiMONO integrated mutation, epigenetics, transcriptomics, proteomics and clinical information, detecting cancer specific omic features. Moreover, we analysed a four-level data set from a major depressive disorder cohort, including genetic, epigenetic, transcriptional and clinical data. Here we demonstrated KiMONo's analytical power to identify expression quantitative trait methylation sites and loci and show it's advantage to state-of-the-art methods. Our results show the general applicability to the full spectrum multi-omics data and demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets. The method is freely available as an R package ( https://github.com/cellmapslab/kimono ).

show abstract

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Cited by 481 publications

References 170 publications

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Plasma metabolomics of presymptomaticPSEN1-H163Y mutation carriers: A pilot study

Knowledge guided multi-level network inference

Contact Info

Product

Resources

About