Identification of Rare Alleles for Genetic Association and Risk

描述（由申请人提供）：全基因组关联研究最近已被应用于检测导致人类疾病易感性的遗传变异，如糖尿病、心脏病和癌症。通过设计，这些关联可以间接获得，因为可能无法观察到影响疾病的基因型。相反，希望是遗传风险因素可能与观察到的基因型数据充分相关，从而可以建立关联。捕获常见变异的标准标记集对于检测风险变异也很常见的关联是强大的，但影响疾病表型的罕见遗传变异可能仍然未被检测到。然而，基因型的特定组合可以有效地“标记”风险易感遗传基因座处的基因型，从而促进关联的检测。虽然附近基因座的基因型之间的相关性对于检测关联是必不可少的，但当试图确定关联的最终来源时，即致病遗传基因座，它们是繁重的。在本申请中，我们概述了一种基于单倍型的统计方法来检测和解剖二元表型和罕见遗传变异之间的关联。此外，我们利用我们的模型来识别风险等位基因的个体携带者。这有助于描述遗传对复杂表型的影响，并为制定初步风险模型提供工具。我们将这些方法应用于肺癌全基因组关联研究的数据。我们的方法，这是计算上易于处理的应用程序的大型现有和即将到来的数据集，将被纳入广泛使用的和免费提供的fastPHASE软件包。 公共卫生相关性：确定复杂的人类疾病的遗传原因需要有效地利用来自全基因组变异调查的可用数据。单倍型变异提供了检测罕见遗传因素对疾病影响的信息，并且可以特别有助于预测哪些个体处于发展疾病的最高风险。

DESCRIPTION (provided by applicant): Genome-wide association studies have recently been applied to detect genetic variants that contribute to the predisposition of human disease, such as diabetes, heart disease, and cancer. By design, these associations may be obtained indirectly, as the disease-influencing genotypes may not be observed. Rather, the hope is that genetic risk factors may be sufficiently correlated with observed genotype data, with which an association may be established. Standard marker sets, which capture common variation, are powerful to detect associations where the risk variants are also common, but rare genetic variants that influence disease phenotypes may remain undetected. However, specific combinations of genotypes can effectively "tag" the genotypes at a risk- predisposing genetic locus, facilitating the detection of association. While the correlations among genotypes at nearby loci are essential for detection of an association, they are burdensome when trying to identify the ultimate sources of association, i.e. causative genetic loci. In this application, we outline a haplotype-based statistical approach to detecting and dissecting association between a binary phenotype and rare genetic variants. In addition, we utilize our model to identify individual carriers of the risk alleles. This serves both to aid in the characterization of the genetic influence on a complex phenotype, as well as to provide a tool for formulating preliminary risk models. We will apply these methods to data from a genome-wide association study of lung cancer. Our methods, which are computationally tractable for the application to large existing and forthcoming data sets, will be incorporated into the widely used and freely available fastPHASE software package. PUBLIC HEALTH RELEVANCE: Identification of the genetic causes of complex human disease requires efficient use of available data from genome-wide surveys of variation. Haplotype variation provides information to detect influence on disease from rare genetic factors, and can be particularly helpful in predicting which individuals are at highest risk for developing disease.