Quantitative Methods for Genetic Epidemiology

检测和了解与人类健康相关的多变量性状的遗传基础至关重要 走向个性化医疗的未来。一个重要的方面是理解多效性(当单个基因 影响不止一个性状)，这可以以多种方式提高对基因的生物学理解，以及 最终推进复杂疾病的预防和治疗。然而，统计方法对 评估一个基因对多个性状的同时影响主要依赖于标准的多变量 不直接涉及生物学问题的分析。我们最近开发了新的统计方法来 评估单核苷酸多态与多个数量性状的关联，克服 标准多变量分析的局限性，以提高对基因如何表现的生物学理解 影响多个相关性状。我们建议在现有经验的基础上开发新的统计方法 允许不同类型的特征(例如，二进制、序数)的方法，以促进人类遗传 研究、利用电子病历等丰富的医疗诊断信息。我们还计划 发展统计学方法，在遗传背景下破译多变量性状的遗传基础 通径分析。为了加强对基因如何影响多变量特征的理解，我们计划开发 并评估因果调解模型，以便提供关于最可能的模型集的指导 “解释”基因与性状的关联，从而为流行病学家和 实验室科学家正在进行后续研究。

Detecting and understanding the genetic basis of multivariate traits related to human health are critical to the future of personalized medicine. An important aspect is understanding pleiotropy (when a single gene influences more than one trait), which can improve the biological understanding of a gene in multiple ways, and ultimately advance prevention and treatment of complex diseases. However, the statistical methods to evaluate the simultaneous impact of a gene on multiple traits have mainly relied on standard multivariate analyses that do not directly address biological questions. We recently developed novel statistical methods to evaluate the association of a single-nucleotide polymorphism with multiple quantitative traits, overcoming the limitation of standard multivariate analysis in order to improve biological understanding of how a gene influences multiple correlated traits. We propose to build on our experience in order to develop new statistical methods that allow for different types of traits (e.g., binary, ordinal) in order to facilitate human genetic research, such as use of the rich medical diagnostic information from electronic medical records. We also plan to develop statistical methods that decipher the genetic basis of multivariate traits in the context of genetic pathway analyses. To enhance understanding of how genes influence multivariate traits, we plan to develop and evaluate causal mediation models in order to provide guidance on the most likely sets of models that “explain” the association of genes with traits, thereby providing much needed guidance to epidemiologists and laboratory scientists on follow-up studies.