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Ge, Tian

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Ge, Tian
Author's Publications: search.filters.isAuthorOfPublication.ca5c1ba1-93ca-4ff8-9c88-0553bc9f607b

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    Phenome-wide heritability analysis of the UK Biobank
    (Public Library of Science, 2017) Ge, Tian; Chen, Chia-Yen; Neale, Benjamin; Sabuncu, Mert R; Smoller, Jordan
    Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.
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    Multidimensional heritability analysis of neuroanatomical shape
    (Nature Publishing Group, 2016) Ge, Tian; Reuter, Martin; Winkler, Anderson M.; Holmes, Avram J.; Lee, Phil; Tirrell, Lee S.; Roffman, Joshua; Buckner, Randy; Smoller, Jordan; Sabuncu, Mert R
    In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.
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    A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes
    (Frontiers Media S.A., 2017) Wang, Changqing; Sun, Jianping; Guillaume, Bryan; Ge, Tian; Hibar, Derrek P.; Greenwood, Celia M. T.; Qiu, Anqi
    Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation.