Approaches To Linkage Analysis For Genome Scans

基因组扫描连锁分析方法

• 批准号: 6502134
• 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/6502134
• 关键词: Hodgkin's disease; cancer registry /resource; genetic markers; genetic models; genome; human data; interview; linkage mapping; neoplasm /cancer genetics; nonHodgkin's lymphoma; quantitative trait loci; questionnaires; 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. A collaboration on a new genetic test procedure, termed the ROMP test, was developed this past year which allows for the estimation of heritability of a single or multi-locus quantitative trait. A case-control study of 1427 pairs was conducted investigating whether relatives of person's affected by Hodgkin's lymphoma were at increased risk of developing cancer.

连锁和关联分析的统计方法Albert金曼Z 01 DE 00689随着用于复杂疾病的遗传作图研究的基因组扫描扩展到使用数千个标记，鉴定了太多的区域，其中实现了适度的p值，但根据Lander & Kruglyak，1995的连锁标准，这些区域不符合暗示性或显著性连锁。一种可能的方法是将来自相关标记群的连锁信息联合收割机组合成“区域”连锁检验。两个这样的类的“区域”的连锁测试统计进行了调查，他们的潜力，以增加功率在连锁测试的QTL在广泛的加性遗传力，和不同的标记位置。第一类纳入了最小t统计量，这是连续H-E t检验统计量的最小值;第二组纳入了H-E t检验统计量的移动平均值ma（t）。使用Genometric Analysis Simulation Program（GASP）来模拟样品。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。每个染色体片段含有20个标记，均匀分布在10厘米的间隔。一个单一的基因座，部分负责表型变异的数量性状，被放置在三个不同的位置之一的第一染色体片段：在左边（标记位点1和2之间），在第一四分位数（标记位点5和6之间）和附近的中心（标记位点10和11之间）。对每个性状位置进行单独的模拟。结果表明，与单位点t检验相比，移动平均标记聚类统计实现了功效的真实的增益，范围在5%至20%之间。最大的收益是从30%到60%的加性遗传力模型。2个连续t检验的移动平均值的功率增益与单个t检验相当，在检测相同研究设计的遗传力水平小10%的性状时，t检验的功率相当。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，在密集基因组筛选中的功率的真实的增益的潜力可以通过实施一些基于标记簇的测试统计来实现。在后续的研究中，我们调查了与H-E同胞对连锁检验相关的p值的随机特性的备择假设下的连锁。本研究的目的是将预期p值（EPV）与该检验程序的显著性水平和功效相关联，从而证明与性状遗传力和标记与性状的距离的非线性函数关系。研究也正在进行调查的遗传性和预期水平的霍奇金病和非霍奇金淋巴瘤，使用瑞典癌症登记。另一项研究正在进行中，调查与随机模式相比，特定的8-核苷酸序列是否出现在簇中。正在使用和比较随机性的具体统计检验。在过去的一年里，开发了一种新的遗传测试程序，称为ROMP测试，它允许估计单个或多个位点数量性状的遗传力。对1427对病例对照研究进行了调查，以确定受霍奇金淋巴瘤影响的人的亲属是否有患癌症的风险增加。