Department of Biostatistics, Yale University, 300 George Street, New Haven, CT 06511, USA
Department of Radiology, University of North Carolina at Chapel Hill, 101 Manning Dr, Chapel Hill, NC 27514, USA.
Biostatistics. 2022 Apr 13;23(2):467-484. doi: 10.1093/biostatistics/kxaa035.
Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer's Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.
遗传力分析在定量遗传学中起着核心作用，用于描述遗传对人类复杂特征的贡献，并在大规模表型下优先进行下游分析。现有研究主要集中在单一表型建模上，目前可用的多变量表型方法往往存在规模和解释方面的问题。在本文中，受理解遗传基础如何影响人类大脑变异的启发，我们开发了一种综合贝叶斯遗传力分析方法，以联合估计高维神经影像学特征的遗传力。为了诱导稀疏性并结合大脑解剖结构，我们基于大脑结构网络和体素相关性，在区域和局部测量之间进行层次选择。我们还使用非参数狄利克雷过程混合模型来实现与单核苷酸多态性相关表型变异的分组，提供了生物学合理性。通过广泛的模拟，我们表明，在各种情况下，该方法在遗传力估计和遗传特征选择方面优于现有方法。我们最后将该方法应用于两个大型成像遗传学数据集：阿尔茨海默病神经影像学倡议和英国生物库，并显示出有生物学意义的结果。
