Sequential Sampling for Optimal Bayesian Classification of Sequencing Count Data

Broumand, Ariana; Dadaneh, Siamak Zamani

doi:10.1109/acssc.2018.8645518

Cited by 3 publications

(2 citation statements)

References 37 publications

(42 reference statements)

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“…Analyses that consider the joint effects of multiple traits can have greater statistical power than single-trait analyses of genomic association studies (13) (14) (15). In addition, assigning probabilistic models to expression levels of a group of genes (usually within one pathway) enables development of more accurate classifiers and clusters (16) (17) (18) (19). For high-dimensional metabolomic data, systems approaches based on Mendelian randomization principles can provide an even more comprehensive analysis.…”

Section: Introductionmentioning

confidence: 99%

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

Yazdani

Elsea

et al. 2019

BMC Genomics

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Background Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. Results The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate ( p -value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. Conclusion Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases. Electronic supplementary material The online version of this article (10.1186/s12864-019-5772-4) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

Yazdani

Elsea

et al. 2019

BMC Genomics

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“…The key feature of causal networks is being discovery-based, and suitable for handling large-scale data, where we have limited knowledge about the underlying interconnectivity. There are different applications of systematic analysis of omics including causal networks ( Zhu et al, 2012 ; Franzén et al, 2016 ; Broumand and Dadaneh, 2018 ; Ahangaran et al, 2019 ; Ahangaran et al, 2020 ; Yazdani et al, 2020 ; Gerring et al, 2021 ). For instance one of the early applications is the integration of genetic variants, metabolites, gene expressions, and proteins on yeast data to identify the underlying molecular networks ( Zhu et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

From classical mendelian randomization to causal networks for systematic integration of multi-omics

Yazdani

Méndez-Giráldez

et al. 2022

Front. Genet.

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The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.

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A Sequential Experimental Design Method Based on Binary Response Prediction

Wang,

Pan,

et al. 2024

2024 10th International Symposium on System Security, Safety, and Reliability (ISSSR)

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Sequential Sampling for Optimal Bayesian Classification of Sequencing Count Data

Cited by 3 publications

References 37 publications

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics

From classical mendelian randomization to causal networks for systematic integration of multi-omics

A Sequential Experimental Design Method Based on Binary Response Prediction

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