标题： 具有维度缩减的FDR控制过程及其在具有连锁不平衡得分的GWAS中的应用 显示英文标题

标题： FDR controlling procedures with dimension reduction and their application to GWAS with linkage disequilibrium score

摘要： 全基因组关联研究（GWAS）已经发现了许多与各种表型和复杂疾病相关的单核苷酸多态性（SNPs）。 然而，所识别的遗传变异并不能完全解释复杂特征的遗传力，这就是所谓的缺失遗传力问题。 为了解决这一挑战，并在最大化真实关联的同时准确控制假阳性，我们提出了两种方法，涉及连锁不平衡（LD）分数作为协变量。 我们应用主成分分析（PCA），一种降维技术，以在高维协变量存在的情况下控制错误发现率（FDR）。 这种方法不仅提供了如何在高维中多个协变量影响FDR控制的便捷解释，而且相比不使用协变量的情况，具有更高的统计功效。 此外，我们旨在通过各种模拟实验研究协变量如何提高统计功效，并将结果与实际数据示例进行比较，以获得更好的解释。 使用现实世界的数据集，包括以体重指数（BMI）作为表型的GWAS，我们评估了我们提出的方法的性能。 通过在FDR控制的GWAS分析中引入LD分数作为协变量，我们证明了它们在选择有信息量的LD分数和提高显著SNPs识别方面的有效性。 我们的方法在保留LD分数的基本信息的同时，减轻了计算负担并增强了可解释性。 总体而言，我们的研究有助于推动GWAS中统计方法的发展，并为希望提高遗传关联分析精度的研究人员提供了实用指导。 显示英文摘要

摘要： Genome-wide association studies (GWAS) have led to the discovery of numerous single nucleotide polymorphisms (SNPs) associated with various phenotypes and complex diseases. However, the identified genetic variants do not fully explain the heritability of complex traits, known as the missing heritability problem. To address this challenge and accurately control false positives while maximizing true associations, we propose two approaches involving linkage disequilibrium (LD) scores as covariates. We apply principal component analysis (PCA), one of the dimensionality reduction techniques, to control the False Discovery Rate (FDR) in the presence of high-dimensional covariates. This method not only provides a convenient interpretation of how multiple covariates in high dimensions affect the control of FDR but also offers higher statistical power compared to cases where covariates are not used. Furthermore, we aim to investigate how covariates contribute to increasing the statistical power through various simulation experiments, comparing the results with real data examples to derive better interpretations. Using real-world datasets, including GWAS with Body Mass Index (BMI) as the phenotype, we evaluate the performance of our proposed approaches. By incorporating LD scores as covariates in FDR-controlled GWAS analyzes, we demonstrate their effectiveness in selecting informative LD scores and improving the identification of significant SNPs. Our methods alleviate computational burden and enhance interpretability while retaining essential information from LD scores. In general, our study contributes to the advancement of statistical methods in GWAS and provides practical guidance for researchers looking to improve the precision of genetic association analyses.