ArticleNature communications2018
Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- TransferGWAS of T1-weighted brain MRI data from UK Biobank.PLoS genetics · 2024Article
- Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex Pedigrees.Human brain mapping · 2024Article
- Testability of high-dimensional linear models with nonsparse structures.Annals of statistics · 2022Article
- Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop.Circulation. Cardiovascular imaging · 2021Article
- Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study.Genes · 2020Article
- Investigating microstructure of white matter tracts as candidate endophenotypes of Social Anxiety Disorder - Findings from the Leiden Family Lab study on Social Anxiety Disorder (LFLSAD).NeuroImage. Clinical · 2020Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 3 countries.
Funding
Abstract
Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.
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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.