Individual-specific liability groups in genetic linkage, with applications to kindreds with Li-Fraumeni syndrome.
Individual-specific liability groups in genetic linkage, with applications to kindreds with Li-Fraumeni syndrome.
复制标题
遗传连锁中的个体特定责任群体，适用于患有李法美尼综合症的亲属。
DOI：
10.1086/339370
复制
发表时间：
2002
影响因子：
9.8
通讯作者：
Strong,LouiseC
中科院分区：
文献类型：
--
作者：
Shete,Sanjay;Amos,ChristopherI;Hwang,Shih-Jen;Strong,LouiseC
在这份报告中，我们提出了一种简单而强大的方法，将特定于个人的责任类别纳入到关联分析中。该方法既适用于数量性状，也适用于质量性状。在连锁研究中，我们可能有关于不同协变量的信息。将这些协变量与残留的家族效应、发病年龄效应和易感性的估计一起纳入责任类别的定义中，可以增加检测遗传连锁的能力。在这项研究中，我们展示了如何形成个人特定的责任类别，并在标准的连锁分析程序中使用这些类别，例如广泛使用的连锁程序包，以执行更强大的遗传连锁分析。我们的模拟研究表明，这种方法产生了更高的LOD分数，并对显示连锁的家系中的重组比例进行了更准确的估计。建议的方法也适用于安德森癌症中心通过儿童软组织肉瘤先证者收集的家系。在这些家系中已经确认了p53肿瘤抑制基因的胚系突变。将我们的方法应用到这些家系中，与没有考虑个体特有协变量信息的分析相比，我们的方法产生了显著更高的LOD评分和更准确的重组分数。
In this report, we present a simple and powerful way to incorporate individual-specific liability classes into linkage analysis. The proposed method is applicable to both quantitative and qualitative traits. In linkage studies, we may have information about different covariates. Incorporation of these covariates along with the estimates of residual familial effects, age-at-onset effects, and susceptibility in the definition of liability classes can increase the power to detect genetic linkage. In this study, we show how one can form individual-specific liability classes and use these classes in standard linkage-analysis programs, such as the widely used LINKAGE package, to perform more powerful genetic linkage analysis. Our simulation study shows that this approach yields higher LOD scores and more-accurate estimates of the recombination fraction in the families showing linkage. The proposed method is also applied to kindreds collected, at the M. D. Anderson Cancer Center, through probands with childhood soft-tissue sarcoma. Confirmed germ-line mutations in the p53 tumor-suppressor gene have been identified in these families. Application of our method to these families yielded significantly higher LOD scores and more-accurate recombination fractions than did analysis that did not account for individual-specific covariate information.