Computational Methods to Detect Epistasis

人类基因组草图序列的完成和人类基因组的持续发展 对单核苷酸多态(SNP)进行快速分型的技术使人们非常乐观地认为，研究人员将能够确定导致许多常见疾病的遗传机制。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、酗酒和哮喘的大基因成分方面，几乎没有成功。 这些复杂的表型很可能与基因相互作用有关，比简单的加法或乘法模型显示的更复杂。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常见疾病的遗传因素，这些疾病给社会带来了巨大的代价，这突显了开发专门用于检测基因相互作用的新方法的必要性。 这项建议的目标是开发和评估检测基因-基因(上位性)相互作用的新方法，并将它们应用于与药物代谢有关的药物遗传学数据。具体地说，其目的是：1)开发统计方法来测试候选基因座之间的上位性，侧重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在模拟数据上的有效性，以及3)在圣路易斯华盛顿大学霍华德·麦克劳德博士领导的药物遗传学研究网络项目(U01 GM63340，药物路径中的功能多态分析)的真实数据上应用和测试这些方法 同样在华盛顿大学的Brian Gage博士领导的华法林代谢研究(R01 HL71038和R01 HL074724)。 要实现这些目标，除了数学技能外，还需要具备统计学专门知识和遗传学和代谢途径的生物学知识，并辅之以用于产生分析数据的实验室技术知识。为此，拟议的奖项将补充卡尔弗豪斯博士之前在数学和流行病学方面的培训，提供统计学和分子生物学方面的培训。这段时期的合作、课程安排和指导经验将使卡尔弗豪斯博士能够在多学科科学团队中发挥领先的量化作用。

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.