More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlated phenotypes that reflect common physiological processes. By jointly analyzing these correlated traits, we can gain more power by aggregating multiple weak effects and shed light on the mechanisms underlying complex human diseases. The majority of existing multi-trait association test methods are based on jointly modeling the multivariate traits conditional on the genotype as covariate, and can readily accommodate the imputed SNPs by using their imputed dosage as a covariate. An alternative class of multi-trait association tests is based on the inverted regression, which models the distribution of genotypes conditional on the covariate and multivariate traits, and has been shown to have competitive performance. To our knowledge, all existing inverted regression approaches have implicitly used the "best-guess" genotypes, which is not efficient and known to lead to dramatic power loss, and there have not been any proposed methods of incorporating imputation uncertainty into inverted regressions. In this work, we propose a general and efficient framework that can account for the imputation uncertainty to further improve the association test power of inverted regression models for imputed SNPs. We demonstrate through extensive numerical studies that the proposed method has competitive performance. We further illustrate its usefulness by application to association test of diabetes-related glycemic traits in the Atherosclerosis Risk in Communities (ARIC) Study.

中文翻译：

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使用估算的 SNP 对多个连续性状进行全基因组关联测试

越来越多的大型队列研究已经或正在进行全基因组关联研究 (GWAS)，以揭示许多复杂人类疾病的遗传成分。这些大型队列研究经常收集大量反映常见生理过程的相关表型。通过联合分析这些相关性状，我们可以通过聚合多个弱效应来获得更多力量，并阐明复杂人类疾病的潜在机制。大多数现有的多性状关联测试方法都基于以基因型为协变量对多变量性状进行联合建模，并且可以通过使用其推算剂量作为协变量来轻松适应推算的 SNP。另一类多特征关联测试基于反向回归，它对以协变量和多变量性状为条件的基因型分布进行建模，并已被证明具有竞争力。据我们所知，所有现有的反向回归方法都隐含地使用了“最佳猜测”基因型，这种方法效率不高且已知会导致显着的功率损失，并且还没有任何提议的方法将插补不确定性合并到反向回归中。在这项工作中，我们提出了一个通用且有效的框架，可以解释插补的不确定性，以进一步提高反向回归模型对插补 SNP 的关联测试能力。我们通过广泛的数值研究证明所提出的方法具有竞争性的性能。