Causal inference on neuroimaging data with mendelian randomisation
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 tha...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2022
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| _version_ | 1797107761037705216 |
|---|---|
| author | Taschler, B Smith, SM Nichols, TE |
| author_facet | Taschler, B Smith, SM Nichols, TE |
| author_sort | Taschler, B |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T07:20:28Z |
| format | Journal article |
| id | oxford-uuid:dbcecb60-70b5-46d3-a5e6-ee05889819c1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:20:28Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:dbcecb60-70b5-46d3-a5e6-ee05889819c12022-09-27T08:55:43ZCausal inference on neuroimaging data with mendelian randomisationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:dbcecb60-70b5-46d3-a5e6-ee05889819c1EnglishSymplectic ElementsElsevier2022Taschler, BSmith, SMNichols, TEWhile 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. |
| spellingShingle | Taschler, B Smith, SM Nichols, TE Causal inference on neuroimaging data with mendelian randomisation |
| title | Causal inference on neuroimaging data with mendelian randomisation |
| title_full | Causal inference on neuroimaging data with mendelian randomisation |
| title_fullStr | Causal inference on neuroimaging data with mendelian randomisation |
| title_full_unstemmed | Causal inference on neuroimaging data with mendelian randomisation |
| title_short | Causal inference on neuroimaging data with mendelian randomisation |
| title_sort | causal inference on neuroimaging data with mendelian randomisation |
| work_keys_str_mv | AT taschlerb causalinferenceonneuroimagingdatawithmendelianrandomisation AT smithsm causalinferenceonneuroimagingdatawithmendelianrandomisation AT nicholste causalinferenceonneuroimagingdatawithmendelianrandomisation |