标题： 非参数降秩回归用于多SNP多性状关联定位 显示英文标题

标题： Nonparametric Reduced-Rank Regression for Multi-SNP, Multi-Trait Association Mapping

摘要： 全基因组关联研究已被证明对于理解疾病的遗传基础至关重要。 然而，许多复杂特征——个性特征、面部特征、疾病亚型——本质上是高维的，阻碍了简单的关联映射方法。 我们开发了一种非参数贝叶斯降秩回归模型，用于多SNP、多特征关联映射，而不需要指定线性子空间的秩。 我们在模拟数据和真实数据中展示了我们的模型在SNP和相关特征之间共享强度，提高了统计功效，以识别遗传关联，并通过可解释的、由SNP监督的高维表型低维线性投影来实现。 在HapMap第3阶段基因表达QTL研究数据中，我们发现了经典单变量检验难以发现且两步方法无法恢复的多效性表达QTL。 我们的Python软件BERRRI可在GitHub上公开获取：https://github.com/ashlee1031/BERRRI. 显示英文摘要

摘要： Genome-wide association studies have proven to be essential for understanding the genetic basis of disease. However, many complex traits---personality traits, facial features, disease subtyping---are inherently high-dimensional, impeding simple approaches to association mapping. We developed a nonparametric Bayesian reduced rank regression model for multi-SNP, multi-trait association mapping that does not require the rank of the linear subspace to be specified. We show in simulations and real data that our model shares strength over SNPs and over correlated traits, improving statistical power to identify genetic associations with an interpretable, SNP-supervised low-dimensional linear projection of the high-dimensional phenotype. On the HapMap phase 3 gene expression QTL study data, we identify pleiotropic expression QTLs that classical univariate tests are underpowered to find and that two step approaches cannot recover. Our Python software, BERRRI, is publicly available at GitHub: https://github.com/ashlee1031/BERRRI.