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.

中文翻译：

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具有高维神经影像学表型的贝叶斯稀疏遗传力分析。

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性状的贡献，并优先考虑大规模表型下的下游分析。现有工作主要集中在对单一表型建模，而当前可用的多变量表型方法通常会受到缩放和解释的影响。在本文中，出于了解遗传基础如何影响人类大脑变异的动机，我们开发了一种综合贝叶斯遗传力分析，以联合估计高维神经影像特征的遗传力。为了诱导稀疏性并结合大脑解剖结构，我们根据大脑结构网络和体素依赖性在区域和局部测量中进行分层选择。我们还使用非参数 Dirichlet 过程混合模型来实现单核苷酸多态性相关表型变异之间的分组，提供生物学合理性。通过广泛的模拟，我们表明所提出的方法在各种情况下的遗传力估计和可遗传性状选择方面优于现有方法。我们最终将该方法应用于两个大型成像遗传学数据集：阿尔茨海默氏病神经影像学计划和英国生物库，并展示了具有生物学意义的结果。