ArticleNeuroImage2015
Fast and powerful heritability inference for family-based neuroimaging studies.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- cuBNM: GPU-Accelerated Brain Network Modeling.bioRxiv : the preprint server for biology · 2025Article
- Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex Pedigrees.Human brain mapping · 2024Article
- Spatial-extent inference for testing variance components in reliability and heritability studies.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Genome-wide significant risk loci for mood disorders in the Old Order Amish founder population.Molecular psychiatry · 2023Article
- Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project.NeuroImage · 2021Article
- Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop.Circulation. Cardiovascular imaging · 2021Article
- Imaging tau burden in dementia with Lewy bodies using [Neurobiology of aging · 2021Article
- Multiple dimensions of stress vs. genetic effects on depression.Translational psychiatry · 2021Article
- Measuring cerebral perfusion with [Brain communications · 2021Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 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
- Obesity and Bone Health Revisited: A Mendelian Randomization Study for Koreans.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2019Article
- Genomic kinship construction to enhance genetic analyses in the human connectome project data.Human brain mapping · 2019Article
- Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data.Frontiers in neuroinformatics · 2019Article
- Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests.Nature communications · 2018Article
- Article
- Heritability Estimation of Reliable Connectomic Features.Connectomics in neuroImaging : second international workshop, CNI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. CNI (Workshop) (2nd : 2018 : Granada, Spain) · 2018Article
- Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes.Nature communications · 2018Article
- Article
- Gray matter heritability in family-based and population-based studies using voxel-based morphometry.Human brain mapping · 2017Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study.
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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.