Quantitative methods for genetic linkage heterogeneity

描述（由申请人提供）：对人类基因组序列的不断理解和基因组技术的最新进展为解决常见人类疾病的遗传基础提供了坚实的基础。然而，聚集在家族中的常见疾病，如许多癌症、心血管疾病和糖尿病，在其病因和表型表达方面具有很大的异质性。这种异质性的来源包括遗传、表型、环境和行为风险因素、研究设计以及所有这些来源的组合。常见疾病的连锁研究的成功是有限的，未来的成功可能取决于解释（和控制）广泛异质性的能力。这项研究的目标是通过开发统计和计算方法来提高识别和表征复杂人类特征易感基因的能力，这些方法可以解释连锁异质性。 目标1.递归分区树：为了更好地识别在家庭子集中起关键作用的易感基因，我们将开发新的定量方法，用于基于递归划分树的子集之间的遗传连锁异质性评估。这些新的发展，现代统计和计算算法的基础上，应提高的权力，检测连锁存在大量的异质性所造成的非遗传因素，影响疾病的非线性方式。 目标二。回归模型：为了提高检测复杂遗传性状连锁的能力，并改进有希望的连锁区域，我们将开发新的统计方法，提供灵活的方法来直接模拟关键因素对遗传连锁研究的血统共享概率的影响。 目标3：用户友好的软件：将向科学界免费提供用于实施拟议方法的方便用户的软件，包括记录良好的程序及其使用实例。

DESCRIPTION (provided by applicant): The evolving understanding of the human genome sequence and recent advances in genomic technologies provide a strong foundation to resolve the genetic basis of common human diseases. However, common diseases that cluster in families, such as many cancers, cardiovascular, and diabetes, have much heterogeneity in their etiology and their phenotypic expression. Sources of this heterogeneity range from genetic, to phenotype, to environmental and behavioral risk factors, to study design, to a combination of all of these sources. The success of linkage studies for common diseases has been limited, and future success may hinge on the ability to account for (and control) a wide range of heterogeneity. The goals of this proposed research are to improve the ability to identify and characterize susceptibility genes for complex human traits by developing statistical and computational methods that account for linkage heterogeneity. Aim 1. Recursive Partitioning Trees: To improve identification of susceptibility genes that play critical roles in subsets of families, we will develop new quantitative methods for evaluation of genetic linkage heterogeneity across subsets based on recursive partitioning trees. These new developments, based on modem statistical and computing algorithms, should improve the power to detect linkage in the presence of a large amount of heterogeneity caused by non-genetic factors that influence disease in non-linear ways. Aim 2. Regression Models: To increase the power to detect linkage for complex genetic traits, and refine the regions of promising linkage, we will develop new statistical methods that provide flexible ways to directly model the influence of critical factors on identity-by-descent sharing probabilities for genetic linkage studies. Aim 3. User-friendly software: User-friendly software that implements the proposed methods, including well-documented procedures and examples of their usage, will be provided free to the scientific community.