METHODS FOR HUMAN GENETIC LINKAGE MAPPING

描述（改编自研究者摘要）：广泛的目标是 1. 提供稳健的、计算上可行的统计方法， 遗传连锁数据分析，和2. 分析优势， 的缺点，改进和扩展现有的方法。 这些措施应进一步促进 定位涉及各种人类特征的遗传基因座的目标， 疾病，并确定其影响和遗传方式。 具体 目标如下： A.通过大规模的等位基因共享来开发复杂性状的定位方法 血统 考虑选择最强大的共享统计数据。 为 多点按血统共享身份，提出具体建议， 这些共享统计信息的属性以及 某些假设。 制定复杂性状精细定位的策略 在那些小的孤立的创始人群体中， 家谱信息，但这是太大的可行的充分 多点分析 考虑各州的身份和人口关联 方法，并提出战略，以确定最强大的方法下， 各种各样的场景。 B。为了改进遗传标记排序中不确定性的评估， 一个强大而有效的方法，不依赖于强有力的假设 干扰 使用贝叶斯方法评估不确定性，联合收割机数据 从不同的研究中，整合地图。 C.开发用于分析人类精子数据的方法和软件。 实施和改进检测偏析变形的方案， 建模干扰，并用于精子数据的稳健标记排序。 使用 这些实施方案用于研究人类遗传参数的异质性， 男性。 D.研究链接程序对以下问题的鲁棒性， 错定的等位基因频率、基因分型错误和干扰。 研究 不完整的数据在加剧错误指定问题中的作用。 查明 在最令人关切的领域，制定诊断和补救措施。

DESCRIPTION (Adapted from the Investigator's Abstract): The broad goals are 1. to provide robust, computationally feasible statistical methods for analysis of genetic linkage data, and 2. to analyze the strengths and faults of, improve, and extend existing methods. These should further the goals of locating genetic loci involved in various human traits and diseases, and determine their effects and modes of inheritance. Specific aims are as follows: A. To develop methods for mapping complex traits by allele-sharing in large pedigrees. Consider selection of most powerful sharing statistics. For multipoint identity-by-descent sharing, make specific proposals regarding properties of such sharing statistics and most powerful statistics under certain assumptions. Develop strategies for fine mapping of complex trait genes in those small isolated founder populations with nearly complete genealogical information, but that are too large for feasible full multipoint analysis. Consider identity-by-state and population association methods and propose strategy to determine most powerful methods under a variety of scenarios. B. To improve assessment of uncertainty in ordering of genetic markers using a robust yet efficient method that does not rely on strong assumptions about interference. Use with Bayesian methods to assess uncertainty, combine data from different studies, integrate maps. C. To develop methods and software for analysis of human sperm data. Implement and improve programs for detecting segregation distortion, modeling interference, and for robust marker ordering with sperm data. Use these implementations to study heterogeneity of genetic parameters in human males. D. To investigate robustness of linkage procedures to problems such as misspecified allele frequencies, genotyping errors, and interference. Study role of incomplete data in exacerbating misspecification problems. Pinpoint areas of greatest concern, develop diagnostics and remedies.