ArticleProceedings of the National Academy of Sciences of the United States of America2015
Massively expedited genome-wide heritability analysis (MEGHA).
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept.Nature neuroscience · 2016Pooled it
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- A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.Genome biology · 2024Article
- Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.Annual review of biomedical data science · 2024Review
- SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.Human brain mapping · 2024Article
- SAN: mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.bioRxiv : the preprint server for biology · 2024Article
- A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.The annals of applied statistics · 2024Article
- Spatial-extent inference for testing variance components in reliability and heritability studies.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Static and dynamic functional connectomes represent largely similar information.bioRxiv : the preprint server for biology · 2023Article
- Heritability Estimation of Cognitive Phenotypes in the ABCD StudyBehavior genetics · 2023Article
- Heritability of human "directed" functional connectome.Brain and behavior · 2023Article
- Static and dynamic fMRI-derived functional connectomes represent largely similar information.Network neuroscience (Cambridge, Mass.) · 2023Article
- Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.Biostatistics (Oxford, England) · 2022Article
- Estimating SNP heritability in presence of population substructure in biobank-scale datasets.Genetics · 2022Article
- Unpaired data empowers association tests.Bioinformatics (Oxford, England) · 2021Article
- Single-Cell RNA Sequencing in Parkinson's Disease.Biomedicines · 2021Review
- Heritability of individualized cortical network topography.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Polygenic Architecture of Human Neuroanatomical Diversity.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Imaging local genetic influences on cortical folding.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.
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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.