A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases.
A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases.
复制标题
在小型到中等大小的血统中与复杂疾病的不同连锁统计的比较。
DOI：
10.1186/1756-0500-5-411
复制
发表时间：
2012-08-06
影响因子：
1.8
通讯作者：
Strauch K
中科院分区：
文献类型：
--
作者：
Flaquer A;Strauch K
在过去的几年中，GWA研究已经成功地识别出与复杂疾病相关的常见SNP。然而，大多数以这种方式发现的变异只占性状变异的一小部分。这一事实导致研究人员专注于大规模测序的稀有变异作图，这可以通过使用连锁信息来促进。问题是，为什么连锁分析在分析复杂疾病时往往无法识别基因。使用模拟，我们研究了参数和非参数连锁统计（KC-LOD，NPL，LOD和MOD评分）的能力，以检测使用不同谱系结构的复杂疾病的基因的影响。正如预期的那样，少于三个受影响个体的少数谱系具有低的能力来定位疾病基因，具有适度的效果。有趣的是，当分析中包括未受影响的个体时，无论真正的遗传模式如何，功效都会降低。此外，我们发现，表现最好的统计不仅取决于类型的谱系，但也对真正的遗传模式。当以一种合理的方式应用时，连锁是一种适当和强大的技术来定位复杂疾病的基因。与关联分析不同，连锁分析不受等位基因异质性的影响。那么，为什么连锁分析在复杂的疾病中经常失败呢？显然，当使用的小谱系数量不足时，人们可能会错过真正的遗传连锁，而实际上存在真实的效应。此外，我们表明，检验统计量有一个重要的影响功率检测联动以及。因此，如果采用的检验统计量不足，连锁分析可能会失败。对于给定的遗传模式和研究中的谱系类型，我们提供了关于最有利的检验统计量的建议，以降低错过真正连锁的概率。
In the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures. As expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance. When applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.