ArticleNeuroImage2020
Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores.Molecular neurodegeneration · 2023Pooled it
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.PLoS genetics · 2026Article
- FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.bioRxiv : the preprint server for biology · 2026Article
- Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Large-Scale Genotype-Based Trait Imputation With Multi-Ancestry GWAS Data.Genetic epidemiology · 2026Article
- Co-expression-wide association studies link genetically regulated interactions with complex traits.Nature communications · 2025Article
- Neuroimaging PheWAS and molecular phenotyping implicatemedRxiv : the preprint server for health sciences · 2025Article
- Extracting Genetically-Imputed Causal Features From ECG Data.Statistical analysis and data mining · 2025Article
- Shared genetics and causal relationship between sociability and the brain's default mode network.Psychological medicine · 2025Article
- Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease.PLoS genetics · 2025Article
- Multivariate proteome-wide association study to identify causal proteins for Alzheimer disease.American journal of human genetics · 2025Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- TIPS: a novel pathway-guided joint model for transcriptome-wide association studies.Briefings in bioinformatics · 2024Article
- DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.Biostatistics (Oxford, England) · 2024Article
- A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.The annals of applied statistics · 2024Article
- Conditional transcriptome-wide association study for fine-mapping candidate causal genes.Nature genetics · 2024Article
- Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data.Genetic epidemiology · 2023Article
- An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset.PLoS genetics · 2023Article
- Deep causal feature extraction and inference with neuroimaging genetic data.Statistics in medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.