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Methods for genetic linkage analysis in the presence of heterogeneity.

机译:存在异质性时的遗传连锁分析方法。

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摘要

Genetic heterogeneity is one of the most significant obstacles to identifying the genetic basis for many common human diseases. Heterogeneity is the term used for genetic systems in which numerous genes each make a small contribution to the overall heritability of a disease. Linkage analysis has been used successfully for several decades to map disease susceptibility genes, but it lacks power to identify susceptibility genes in heterogeneous systems. The purpose of this research is to improve current methods and develop new methods for linkage analysis in the presence of genetic heterogeneity. Competency is established in conventional and emerging methodology, new methods are developed, and the newly developed methods are tested in real study data. Prostate cancer (PCa), a prime example of the problems that heterogeneity creates for genetic epidemiologists, is used as a model system throughout the research.;Studying alternate PCa phenotype definitions or PCa subtypes may improve our knowledge of the disease. Chapter 2 describes a conventional linkage analysis for aggressive PCa subtypes, the results of which confirm two previously reported PCa aggressiveness loci. Chapter 3 presents proof of concept that phenotypes based on gene expression profiles from microarray data may be useful for identifying genes associated with risk of PCa development via linkage analysis. Chapters 4 and 5 describe the development and application of the innovative sumLINK statistic, which identifies genetic regions of extreme consistency across pedigrees without regard to negative evidence from unlinked or uninformative pedigrees. Significance of the sumLINK statistic and the complimentary sumLOD statistic is determined empirically by an innovative permutation procedure that randomizes linkage information across pedigrees. Simulation testing shows that this method is reliable and powerful for finding genes in heterogeneous systems. The utility of the sumLINK method is demonstrated with exciting results using data from the International Consortium for Prostate Cancer Genetics for aggressive and general prostate cancer. The sumLINK procedure fills an important informatics role by facilitating secure interinstitutional data sharing and collaborative research. The sumLINK method is a powerful tool for combating the obstacles presented by heterogeneity, and will improve our knowledge of the genetic epidemiology of many common, complex diseases.
机译:遗传异质性是鉴定许多常见人类疾病遗传基础的最重大障碍之一。异质性是用于遗传系统的术语,其中许多基因各自对疾病的总体遗传力贡献很小。连锁分析已成功用于定位疾病易感基因数十年,但缺乏识别异构系统中易感基因的能力。本研究的目的是在存在遗传异质性的情况下改进当前方法并开发用于连锁分析的新方法。在常规方法和新兴方法中建立了能力,开发了新方法,并在实际研究数据中测试了新开发的方法。前列腺癌(PCa)是异质性给遗传流行病学家造成的问题的一个主要例子,在整个研究中被用作模型系统。研究替代的PCa表型定义或PCa亚型可能会提高我们对该病的认识。第2章介绍了侵略性PCa亚型的常规连锁分析,其结果证实了两个先前报道的PCa侵袭性基因座。第3章提供了概念证明,即基于微阵列数据的基因表达谱的表型​​可能有助于通过连锁分析来鉴定与PCa发育风险相关的基因。第4章和第5章介绍了创新的sumLINK统计数据的开发和应用,该统计数据确定了谱系中极度一致的遗传区域,而不考虑来自无关联或无信息谱系的负面证据。 sumLINK统计数据和补充sumLOD统计数据的重要性通过创新的置换程序凭经验确定,该程序将谱系之间的链接信息随机化。仿真测试表明,该方法在异构系统中寻找基因是可靠且强大的。使用国际前列腺癌遗传学联盟针对侵略性和一般性前列腺癌的数据,sumLINK方法的实用性得到了令人兴奋的结果。 sumLINK程序通过促进安全的机构间数据共享和协作研究来扮演重要的信息学角色。 sumLINK方法是解决异质性障碍的有力工具,将提高我们对许多常见,复杂疾病的遗传流行病学的了解。

著录项

  • 作者

    Christensen, Gerald Bryce.;

  • 作者单位

    The University of Utah.;

  • 授予单位 The University of Utah.;
  • 学科 Biology Biostatistics.;Biology Bioinformatics.;Biology Genetics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 171 p.
  • 总页数 171
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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