Attempts to identify and prioritize functional DNA elements in coding and noncoding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles may vary widely from one variant to another, making it challenging to summarize different aspects of variant function. Here we propose Multi-dimensional Annotation Class Integrative Estimation (MACIE), an unsupervised multivariate mixed model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and noncoding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics, and estimates the joint posterior functional probability vector of each genomic position, a quantity that offers richer and more interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping using lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

中文翻译：

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用于预测人类基因组中功能变异的多维综合评分框架

尤其是通过使用计算机模拟功能注释数据来尝试识别编码区和非编码区中的功能性DNA元素并对其进行优先级排序的尝试，继续在普及。但是，特定功能角色可能从一个变体到另一变体变化很大，因此很难概括变体功能的不同方面。在这里，我们提出了多维注释类综合估计（MACIE），这是一种无监督的多元混合模型框架，能够集成各种来源的注释来评估编码和非编码变体的多维功能角色。与现有的一维评分方法不同，MACIE将变体功能视为包含多个特征的复合属性，并估算每个基因组位置的联合后验功能概率矢量，在功能的多个方面都存在的情况下，提供更丰富，更易解释的信息的数量。MACIE应用于各种独立的编码和非编码数据集，在区分功能性和非功能性变体方面展示了强大而强大的性能。我们还展示了MACIE在使用来自欧洲遗传和基因流行病学联盟网络的脂质GWAS摘要统计数据进行精细映射的应用。