存在缺失观测的家系数据中多变量表型的映射
Mapping Multivariate Phenotypes in the Presence of Missing Observations for Family-Based Data
AI总结：
针对家系数据中表型缺失的问题，本研究探索基于可用表型（连续、计数、分类型）分别用条件正态、泊松、逻辑模型估计缺失值，再进行基于传递的关联检验，通过模拟评估插补策略对检验功效的影响。
摘要：
临床终点性状通常以定量或定性前体为特征，有观点认为分析这些前体性状可能是一种统计上更有力的策略，以破解潜在复杂终点性状的遗传结构。虽然针对定量和定性性状的关联方法已被广泛开发用于群体水平数据，但针对家系水平数据的此类方法的开发是当前研究热点，因为家系数据存在额外挑战，即需要纳入家庭内性状值的相关性。Haldar和Ghosh（2015）开发了一种检验方法，该方法在统计上等价于经典TDT（传递不平衡检验），适用于定量性状和多变量表型。该模型不需要对表型的概率分布进行先验假设。然而，在实际中经常出现的情况是，核心家庭中并非所有后代都有可用的目标表型数据。在本研究中，我们探索了基于可用表型估计缺失表型的方法，并对“完整”数据进行基于传递的关联检验。我们考虑三种类型的表型：连续型、计数型和分类型。对于缺失的连续型表型，使用条件正态模型估计性状值；对于缺失的计数型表型，使用条件泊松模型估计性状值；对于缺失的分类型表型，使用条件逻辑模型估计表型状态的风险。我们将在广泛的遗传模型下进行模拟，评估所提出的插补策略对关联检验功效的影响，并与无缺失数据的理想情况进行比较。
英文摘要：
Clinical end-point traits are often characterized by quantitative or qualitative precursors and it has been argued that it may be statistically a more powerful strategy to analyze these precursor traits to decipher the genetic architecture of the underlying complex end-point trait. While association methods for both quantitative and qualitative traits have been extensively developed to analyze population level data, development of such methods are of current research interest for family-level data that pose additional challenges of incorporation of correlation of trait values within a family. Haldar and Ghosh (2015) developed a test which is Statistical equivalent of the classical TDT for quantitative traits and multivariate phenotypes. The model does not require a priori assumptions on the probability distributions of the phenotypes. However, it may often arise in practice that data on the phenotype of interest may not be available for all offspring in a nuclear family. In this study, we explore methodologies to estimate missing phenotypes conditioned on the available ones and carry out the transmission-based test for association on the 'complete' data. We consider three types of phenotypes: continuous, count and categorical. For a missing continuous phenotype, the trait value is estimated using a conditional normal model. For a missing count phenotypes, the trait value is estimated using a conditional Poisson model. For a missing categorical phenotype, the risk of the phenotype status is estimated using a conditional logistic model. We shall carry out simulations under a wide spectrum of genetic models and assess the effect of the proposed imputation strategy on the power of the association test vis-à-vis the the ideal situation with no missing data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
