A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

Mo, Chen; Ye, Zhenyao; Ke, Hongjie; Lu, Tian; Canida, Travis; Liu, Song; Wu, Qiong; Zhao, Zhiwei; Ma, Yizhou; Hong, L. Elliot; Kochunov, Peter; Ma, Tianzhou; Chen, Shuo

doi:10.1142/9789811250477_0008

Cited by 5 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We identified significant causal effects of SS and CPD on BAG based on MR analysis, consistent with the strong associations reported in previous work [17, 62, 76, 77]. For example, the pack‐year was reported as the significant risk factor associated with BAG, given an increase of 0.36 months of adjusted BAG associated with one pack‐year [17].…”

Section: Discussionsupporting

confidence: 88%

“…These results reinforced the evaluation of the impact coming from smoking and assisted in guiding future studies. This study provided a greater understanding of the causal effects of smoking behaviors on brain aging and cognitive disorders, connecting previous findings of neuroimaging and cognitive function [62, 76, 77]. Our results have important implications in how behavior may also improve neurological health status.…”

Section: Discussionsupporting

confidence: 73%

“…On average, mean

∆ G_{adj}

of nonsmokers was 0.66 (CI = 0.40, 0.93), 1.03 (0.77, 1.28) and 1.10 (0.81, 1.38) years younger than the

∆ G_{adj}

of smokers in aged 40–49, 50–59 and 60–69 categories, respectively (see Cohen’s d in Figure 2c for the effect size). Also,

∆ G_{adj}

was significantly associated with cognitive function (

\overset{β}{true}

= −0.04; CI = −0.06, −0.02; P ‐value = 6.52 × 10 −7 , data not shown), given the cognitive function represented by the intelligence g calculated in our previous work [62].…”

Section: Resultsmentioning

confidence: 82%

“…SS and CPD) on

∆ G_{adj}

in this study. In addition, we explored the association between the adjusted BAG and cognitive function measured from our previous study [62] at the 0.05 significance level. The cognitive function was represented by the intelligence g ‐factor estimated via factor analysis based on cognitive traits related to four domains in the UKB cohort: processing speed, perceptual reasoning, executive function and fluid intelligence [62].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank

Wang

et al. 2022

Addiction

View full text Add to dashboard Cite

Background and Aims: Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.Design: Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 73%

“…On average, mean

∆ G_{adj}

of nonsmokers was 0.66 (CI = 0.40, 0.93), 1.03 (0.77, 1.28) and 1.10 (0.81, 1.38) years younger than the

∆ G_{adj}

of smokers in aged 40–49, 50–59 and 60–69 categories, respectively (see Cohen’s d in Figure 2c for the effect size). Also,

∆ G_{adj}

was significantly associated with cognitive function (

\overset{β}{true}

= −0.04; CI = −0.06, −0.02; P ‐value = 6.52 × 10 −7 , data not shown), given the cognitive function represented by the intelligence g calculated in our previous work [62].…”

Section: Resultsmentioning

confidence: 82%

“…SS and CPD) on

∆ G_{adj}

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank

Wang

et al. 2022

Addiction

View full text Add to dashboard Cite

show abstract

“…Neuroimaging MR analyses in particular are thus likely to require careful consideration of potential pleiotropic effects. Recent studies have relied on multi-variable MR approaches to account for (known) pleiotropy between multiple IDPs(Mo et al 2021). Additionally, high-level phenotypes can be assumed to be more likely to exhibit non-linear associations with SNPs.…”

mentioning

confidence: 99%

Causal Inference on Neuroimaging Data with Mendelian Randomisation

Taschler

Smith

Nichols

2022

Preprint

View full text Add to dashboard Cite

While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.

show abstract

Deep causal feature extraction and inference with neuroimaging genetic data

Yao

Charkraborty

Zhang

et al. 2023

Statistics in Medicine

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a severe public health issue in the world. Magnetic Resonance Imaging (MRI) offers a way to study brain differences between AD patients and healthy individuals through feature extraction and comparison. However, in most previous works, the extracted features were not aimed to be causal, hindering biological understanding and interpretation. In order to extract causal features, we propose using instrumental variable (IV) regression with genetic variants as IVs. Specifically, we propose Deep Feature Extraction via Instrumental Variable Regression (DeepFEIVR), which uses a nonlinear neural network to extract causal features from three‐dimensional neuroimages to predict an outcome (eg, AD status in our application) while maintaining a linear relationship between the extracted features and IVs. DeepFEIVR not only can handle high dimensional individual‐level data for model building, but also is applicable to GWAS summary data to test associations of the extracted features with the outcome in subsequent analysis. In addition, we propose an extension of DeepFEIVR, called DeepFEIVR‐CA, for covariate adjustment (CA). We apply DeepFEIVR and DeepFEIVR‐CA to the Alzheimer's Disease Neuroimaging Initiative (ADNI) individual‐level data as training data for model building, then apply to the UK Biobank neuroimaging and the International Genomics of Alzheimer's Project (IGAP) AD GWAS summary data, showcasing how the extracted causal features are related to AD and various brain endophenotypes.

show abstract

A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

Cited by 5 publications

References 0 publications

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank

Causal Inference on Neuroimaging Data with Mendelian Randomisation

Deep causal feature extraction and inference with neuroimaging genetic data

Contact Info

Product

Resources

About