Learning Causal Biological Networks With the Principle of Mendelian Randomization

Badsha, Md. Bahadur

doi:10.3389/fgene.2019.00460

Cited by 44 publications

(53 citation statements)

References 45 publications

(64 reference statements)

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“…CAUSE is a recent extension of MR that uses genome-wide summary statistics to model causal effects while accounting for pleiotropy 20 . Another type of algorithms address gene network inference by joint analysis of genetic variants and gene expression data in order to learn a large-scale graphical model with causal links among genes [21][22][23] . Recently, Howey et al explored similar methodology as an alternative for MR 24 .…”

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confidence: 99%

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

et al. 2021

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Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

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confidence: 99%

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

et al. 2021

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“…Exploratory analysis (S15 Fig) using data from Simulation Study 3 suggested that the LCV test of GCP = 0 was not well-powered in this scenario, possibly because the concept of a genetic causality proportion (GCP) was not directly encapsulated by our simulation model. We are not the first to propose the utility of genetic anchors to help orient relationships between variables in the context of network analysis [43][44][45]47]. However, to our knowledge, this is the first study to directly compare the performance of MR and BN in both real and simulated data.…”

Section: Discussionmentioning

confidence: 98%

“…This feature is particularly useful when the study aims to address simultaneous causal relationships in "omics"scale data sets, for example in studies of gene expression [41] or metabolites [42]. Recent network-building methods have been developed that allow the analysis of potentially hundreds of variables, including both discrete and continuous data types, taking advantage of the ability of genetic variables to operate as causal anchors to help orient the direction of relationships between non-genetic variables [43][44][45][46][47]. These BN approaches could be considered complementary to the MR-based approaches [28] that enable the construction of such networks, as they use very different algorithms, are more restrictive in terms of requiring individual level input data, and produce different outputs, albeit in order to achieve similar goals.…”

Section: Plos Geneticsmentioning

confidence: 99%

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

et al. 2020

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Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bidirectional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring PLOS GENETICS

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“…The second algorithm (MRPC), proposed by Badsha and Fu [5], was first used in genetic data. This algorithm, identically to PC, has two distinct phases.…”

Section: Bayesian Network Based Causal Discovery Algorithmsmentioning

confidence: 99%

Causal discovery in machine learning: Theories and applications

Nogueira¹,

Gama²,

Ferreira³

2021

JDG

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Determining the cause of a particular event has been a case of study for several researchers over the years. Finding out why an event happens (its cause) means that, for example, if we remove the cause from the equation, we can stop the effect from happening or if we replicate it, we can create the subsequent effect. Causality can be seen as a mean of predicting the future, based on information about past events, and with that, prevent or alter future outcomes. This temporal notion of past and future is often one of the critical points in discovering the causes of a given event. The purpose of this survey is to present a cross-sectional view of causal discovery domain, with an emphasis in the machine learning/data mining area.

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Learning Causal Biological Networks With the Principle of Mendelian Randomization

Cited by 44 publications

References 45 publications

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

Causal discovery in machine learning: Theories and applications

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