STATISTICAL METHODS AND APPLICATIONS IN HUMAN GENETICS

数学统计方法的发展与应用 对复杂人类性状的遗传分析提出了更高的要求。在……里面 特别是，主要的关注点是开发用于 通过基因理解复杂的遗传模式 连锁标记。限制片段长度的出现 现在，覆盖整个基因组的多态(RFLP)提供了 实现这种分析的能力。 具体地说，我们打算：详细说明继承模型 涉及多个基因座(例如，上位性和遗传性 异质性模型)；确定检测链接的能力 如此复杂的模型；决定建立复杂模型的能力 具有多个连锁标记的遗传模式。我们还将 检测可变的发病年龄作为遗传疾病的潜在指标 异质性及其对连锁分析的影响。最后，我们 我将考察多变量的连锁分析的应用 性状，并决定连锁解释遗传的能力 性状间相关性的来源。 所开发的方法将应用于三个主要数据 布景。首先，包括家族史在内的多变量数据 特发性扭转肌张力障碍的检查目标是 建立遗传异质性；这将具有重要的 对拟议的家庭和关联研究的影响。第二，一个 关于乳腺癌的大样本家庭数据(4700名先证者)将 被检查以确定遗传异质性的可能性 由发病年龄和可能的其他危险因素决定； 这些结果将对乳腺癌的连锁分析产生影响。 第三，一组关于免疫反应的广泛数据 南美印第安人肺炎球菌抗原与人类白细胞抗原、GM和KM的关系 将从多变量遗传的角度对家庭进行分析 以确定相关的遗传模式。人类白细胞抗原、粒细胞巨噬细胞的作用 并确定Km在家系中的分布模式。 预计所开发的方法将具有广泛的 上述特定项目以外的适用性。这个 该项目的结果将具有重要的临床和公众意义。 健康很重要，因为它们将提供更好的理解 疾病病因学和(可能复杂的)遗传因素 以及为治疗设计、预防、 和准确的风险评估。

Development and application of mathematical and statistical methods for the genetic analysis of complex human traits is proposed. In particular, the primary focus is on developing methods for understanding complex patterns of inheritance through genetic linkage markers. The advent of restriction fragment length polymorphisms (RFLP's) spanning the entire genome now offers the power to achieve such analysis. Specifically, we intend to: elaborate models of inheritance involving multiple loci (for example, epistasis and genetic heterogeneity model); determine the power of detecting linkage for such complex models; determine the ability to establish complex patterns of inheritance with multiple linked markers. We will also examine variable age of onset as a potential indicator of genetic heterogeneity, and its impact on a linkage analysis. Finally, we will examine the application of linkage analysis for multivariate traits, and determine the power of linkage to explain genetic sources of correlation among traits. The methods that are developed will be applied to three major data sets. First, multivariate data including family history on idiopathic torsion dystonia will be examined with the goal of establishing genetic heterogeneity; this will have important ramifications for proposed family and linkage studies. Second, a large sample of family data on breast cancer (4700 probands) will be examined to determine the possibility of genetic heterogeneity as determined by age of onset and possibly other risk factors; these results will impact on linkage analyses of breast cancer. Third, an extensive set of data on immune response to twelve pneumococcal antigens and HLA, Gm and Km in South American Indian families will be analyzed from a multivariate-genetic standpoint to determine genetic patterns of correlation. The role of HLA, Gm and Km in the familial patterns will be determined. It is anticipated that the methods developed will have broad applicability beyond the specific projects described above. The results of this project will be of significant clinical and public health importance because they will offer a better understanding of disease etiology and the (possibly complex) genetic contribution to it, as well as provide avenues for treatment design, prevention, and accurate risk assessment.