STATISTICAL APPROACHES TO LINKAGE ANALYSIS FOR GENOME

基因组连锁分析的统计方法

• 批准号: 6413830
• 负责人: ALBERT KINGMAN
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF DENTAL & CRANIOFACIAL RESEARCH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6413830
• 关键词: Hodgkin's disease; genetic markers; genetic models; genome; human data; linkage mapping; neoplasm /cancer genetics; nonHodgkin's lymphoma; quantitative trait loci; statistics /biometry

Statistical Methods for Linkage and Association Analyses Albert Kingman Z01DE00689 As genome scans used for genetic mapping studies for complex diseases expand to the use of thousands of markers, too many regions are identified in which modest p-values are realized but fail to qualify for either suggestive or significant linkage by the linkage criteria of Lander & Kruglyak, 1995. One possible approach is to combine information of linkage from clusters of correlated markers into 'regional' linkage tests. Two such classes of 'regional' linkage test statistics were investigated for their potential to increase power in linkage tests for a QTL over a wide range of additive heritability, and different marker locations. The 1st class incorporated min t statistics, which were minimums of consecutive H-E t-test statistics; the 2nd set incorporated moving averages, ma(t), of the H-E t-test statistics. The Genometric Analysis Simulation Program (GASP) was used to simulate the samples. Two distinct chromosomal segments were simulated in these experiments under several genetic models. Each chromosomal segment contained 20 markers uniformly distributed at 10-cM intervals. A single locus, responsible in part for the phenotypic variation of a quantitative trait, was placed at one of three different locations on the 1st chromosomal segment: at the left edge (between marker loci 1 and 2), at the 1st quartile (between marker loci 5 and 6) and near the center (between marker loci 10 and 11). Separate simulations were run for each trait location. The results showed that real gains in power, ranging between 5% and 20% were realized by the moving average marker-cluster statistics compared with the single locus t-test. The greatest gains were for additive heritability models ranging from 30% to 60%. The gain in power of the moving average of 2 consecutive t-tests over a single t-test was comparable achieving an equivalent power for the t-test in detecting a trait having a 10% smaller heritability level for the same study design. The min t statistic based on pairs of consecutive t-test statistics also produced similar gains in power, but the min t statistics based on 3 or more consecutive t-test statistics produced losses in power. Thus, the potential for real gains in power in dense genome screens may be realized by the implementation of some marker-cluster based test statistics. In a follow-up study we investigated the stochastic properties of p-values associated with the H-E Sib-pair linkage test under the alternative hypothesis of linkage. The purpose of this investigation is to relate the expected p-values (EPV) to the significance level and power for this test procedure, demonstrating irts functional relationship with trait heritability and distance of marker from the trait. Studies are also underway investigating the heritability and anticipation levels of Hodgkin's disease and non-Hodgkin's lymphoma, using a Swedish Cancer Registry. Another study investigating whether or not the occurrences of a specific sequence of 8-nucleotides appears in clusters as compared with a random pattern are in progress. Specific statistical tests of randomness are being used and compared.

连锁和关联分析的统计方法阿尔伯特·金曼Z01DE00689随着用于复杂疾病遗传图谱研究的基因组扫描扩展到数千个标记的使用，太多的区域被识别，在这些区域中实现了适度的p值，但根据Lander&Krugarak，1995的连锁标准，未能符合建议或显著连锁的条件。一种可能的方法是将来自相关标记簇的连锁信息结合到“区域”连锁测试中。研究了两类“区域”连锁检验统计量在大范围的加性遗传力和不同标记位置上提高QTL连锁检验能力的潜力。第一类包含最小t统计量，它是连续H-E t检验统计量的最小值；第二类包含H-E t检验统计量的移动平均值ma(T)。基因计量学分析 使用模拟程序(GAP)对样品进行模拟。在这些实验中，在几种遗传模型下模拟了两个不同的染色体片段。每条染色体片段包含20个标记，以10 cM的间隔均匀分布。一个部分负责数量性状表型变异的单基因座被放置在第一个染色体片段上的三个不同位置之一：左侧边缘(标记之间 座位1和2)，在第一个四分位数(在标记座位5和6之间)和靠近中心(在标记座位10和11之间)。对每个性状位置进行了单独的模拟。结果表明，与单基因座t检验相比，移动平均标记-聚类统计法获得了5%~20%的实际功率增益。最大的收益是加性遗传力模型，范围在30%到60%之间。移动的力量的增加 对于同一研究设计，平均两次连续的t-检验具有可比性，在检测遗传力水平较小10%的性状时，实现了与t-检验相同的功率。基于连续t检验统计量对的最小t统计量也产生类似的功率增益，但是基于3个或更多连续t检验统计量的最小t统计量产生功率损失。因此，通过实施一些基于标记簇的测试统计，可以实现在密集基因组筛查中真正获得力量的潜力。在后续研究中，我们研究了在另一种连锁假设下，与H-E同胞连锁检验相关的p值的随机性质。本研究的目的是将期望P值(EPV)与该检验程序的显著水平和功效联系起来，论证IRTS与性状遗传力和标记与性状的距离之间的函数关系。瑞典癌症登记处还在调查霍奇金氏病和非霍奇金淋巴瘤的遗传性和预期水平。另一项研究正在进行中，该研究调查特定的8-核苷酸序列是否出现成簇，而不是随机模式。随机性的具体统计检验正在被使用和比较。