Gene expression profiling of The Cancer Genome Atlas supports an inverse association between body mass index (BMI) and major oesophageal tumour subtypes

Seim, Inge; Jeffery, Penny L.; Chopin, Lisa K.

doi:10.1101/378778

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…The trans gene SYNC was found to interact with dystrobrevin (DMD gene) in order to maintain muscle function (during contraction) in mice as well as being associated with neuromuscular disease [134,135]. In addition, in Seim et al (2018) [136], the authors investigated the relationship between obesity and cancer subtypes and found that PHETA1/FAM109A expression is associated with body mass index (BMI) in esophageal carcinoma in data from The Cancer Genome Atlas (TCGA). PHOSPHO1 has been found to be involved in metabolism, specifically in energy homeostasis [137] and has also been associated via DNA methylation with BMI [138] and HDL levels, which in turn have been negatively associated with LBM [139].…”

Section: Application To Gtex Skeletal Muscle Tissuementioning

confidence: 99%

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

et al. 2022

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Background Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining 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 can improve 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. Methods We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. Results Our benchmarks showed that prior-based strategies outperform methods without prior information 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. Conclusions We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.

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Section: Application To Gtex Skeletal Muscle Tissuementioning

confidence: 99%

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

et al. 2022

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

Hawe

Saha

Waldenberger

et al. 2020

Preprint

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36Background: Molecular multi-omics data provide an in-depth view on biological 37 systems, and their integration is crucial to gain insights in complex regulatory processes. 38 These data can be used to explain disease related genetic variants by linking them 39 to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks 40 regulating cellular processes leave footprints in QTL results as so-called trans -QTL 41 hotspots. Reconstructing these networks is a complex endeavor and use of biological 42 prior information has been proposed to alleviate network inference. However, previous 43 efforts were limited in the types of priors used or have only been applied to model 44 systems. In this study, we reconstruct the regulatory networks underlying trans -QTL 45 hotspots using human cohort data and data-driven prior information. 46Results: We devised a strategy to integrate QTL with human population scale 47 multi-omics data and comprehensively curated prior information from large-scale bio-48 logical databases. State-of-the art network inference methods applied to these data and 49 priors were used to recover the regulatory networks underlying trans -QTL hotspots. We 50 benchmarked inference methods and showed, that Bayesian strategies using biologically-51 informed priors outperform methods without prior data in simulated data and show 52 better replication across datasets. Application of our approach to human cohort data 53 highlighted two novel regulatory networks related to schizophrenia and lean body mass 54 for which we generated novel functional hypotheses. 55 Conclusion: We demonstrate, that existing biological knowledge can be leveraged 56 for the integrative analysis of networks underlying trans associations to deduce novel 57 hypotheses on cell regulatory mechanisms. 58 machine learning, personalized medicine 60 2 Background 61Genome-wide associations studies (GWAS) have been tremendously successful in discover-62 ing disease associated genetic loci. However, establishing causality or obtaining functional 63 explanations for GWAS SNPs is still challenging. In recent years, the focus has shifted from 64 discovery of disease loci to mechanism and explanation, and large efforts have been put 65 into unravelling the functional consequences of GWAS SNPs [1, 2]. These have been made 66 possible through technological advances in measuring genome-wide molecular data in large 67 population cohorts, which further led to a steady increase in biological resources providing 68 simultaneous measurements of different molecular layers (often termed multi-omics data). 69 To elucidate disease mechanisms, systems genetics approaches seek to link GWAS SNPs to 70 intermediate molecular traits by identifying quantitative trait loci (QTL) [3, 4], for example 71 for gene expression levels (eQTL) [5][6][7] or DNA methylation at CpG dinucleotides (meQTL) 72 [8][9][10]. 73 Genetic variants that are QTL for quantitative molecular phenotypes that reside on a 74 different chromosome are called t...

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Gene expression profiling of The Cancer Genome Atlas supports an inverse association between body mass index (BMI) and major oesophageal tumour subtypes

Cited by 2 publications

References 29 publications

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

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

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

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