Computational Methods to Detect Epistasis

人类基因组序列草图的完成和 单核苷酸多态性（SNP）快速基因分型技术使人们对研究人员能够确定导致许多常见疾病的遗传机制感到非常乐观。然而，到目前为止，在寻找基因来解释常见的复杂疾病（如癌症、糖尿病、抑郁症、酗酒和哮喘）的大量遗传成分方面几乎没有成功。 这些复杂的表型可能与基因相互作用有关，比简单的加性或乘法模型所建议的更复杂。虽然使用单基因座模型在孟德尔疾病方面取得了很大进展，但这些方法未能确定许多对社会造成巨大损失的常见疾病的遗传因素，这突出表明需要开发专门针对检测基因相互作用的新方法。 本提案的目标是开发和评估新的方法，用于检测基因-基因（上位）相互作用，并将其应用于药物代谢相关的药物遗传学数据。具体而言，目标是：1）开发测试候选基因座之间的上位性的统计方法，集中于控制与校正多个测试相关的功率损失的方法，2）评估这些检测和推断方法对模拟数据的有效性，和3）应用和测试这些方法的真实的数据从药物遗传学研究网络项目领导的霍华德麦克劳德博士在华盛顿大学在圣路易斯。Louis（U 01 GM 63340，药物途径中的功能多态性分析）和来自 由Brian Gage博士领导的华法林代谢研究，也在华盛顿大学（R 01 HL 71038和R 01 HL 074724）。 要实现这些目标，除了数学技能外，还需要统计专门知识以及遗传学和代谢途径方面的生物学知识，并辅之以用于生成分析数据的实验室技术知识。为此，拟议中的奖项将补充卡尔弗豪斯博士以前在数学和流行病学的培训与统计学和分子生物学的培训。从合作，课程和指导这一时期的经验将使卡尔弗豪斯博士在多学科科学团队中发挥领先的定量作用。

The completion of the draft sequence of the human genome and the continuing development of technologies to rapidly genotype single nucleotide polymorphisms (SNP) have led to great optimism that researchers will be able to determine the genetic mechanisms contributing to many common diseases. However, thus far there has been little success in finding genes to account for the large genetic components of common, complex diseases such as cancer, diabetes, depression, alcoholism, and asthma. These complex phenotypes are likely to be associated with gene interactions more complicated than simple additive or multiplicative models suggest. While great progress had been made on Mendelian diseases using single-locus models, the failure of these methods to determine the genetic contributors to many of the common diseases that are so costly to society underscores the need to develop new methodologies specifically aimed at detecting gene interactions. The goal of this proposal is to develop and evaluate novel methods for detecting gene-gene (epistatic) interactions and to apply them to pharmacogenetic data relating to drug metabolism. Specifically, the aims are: 1) to develop statistical methods to test for epistasis between candidate loci, focusing on methods to control the loss of power associated with correcting for multiple tests, 2) to evaluate the effectiveness of these detection and inference methods on simulated data, and 3) to apply and test these methods on real data from the Pharmacogenetics Research Network project headed by Dr. Howard McLeod at Washington University in St. Louis (U01 GM63340, Functional Polymorphism Analysis in Drug Pathways) and from warfarin metabolism studies headed by Dr. Brian Gage, also at Washington University (R01 HL71038 and R01 HL074724). Achieving these aims will require, in addition to mathematical skills, both statistical expertise and biological knowledge of genetics and metabolic pathways supplemented by knowledge of the laboratory techniques used to generate data for analysis. To this end, the proposed award will supplement Dr. Culverhouse's previous training in mathematics and epidemiology with training in statistics and molecular biology. Experience from this period of collaboration, coursework, and mentoring will enable Dr. Culverhouse to play a leading quantitative role in multi-disciplinary scientific teams.