A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data.
A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data.
复制标题
使用选定样本检测遗传研究中次级表型关联的灵活似然框架：应用于序列数据。
DOI：
10.1038/ejhg.2011.211
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Leal,SuzanneM
中科院分区：
文献类型：
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作者：
Liu,DajiangJ;Leal,SuzanneM
对于使用下一代测序的大多数复杂性状关联研究，除了主要的感兴趣表型外，还可以获得许多临床上重要的次要性状，可以对其进行分析以定位易感基因。由于测序成本高，大多数研究使用选定的样本，这些研究的采样机制可能很复杂。当主要和次要性状相关时，次要表型的分析可能会在选定的样品中引起虚假的关联，现有的方法不足以调整它们。为了解决这个问题，开发了一种基于似然的方法，多性状关联（MTA）。MTA是灵活的，可以应用于任何研究与已知的采样机制。它还允许遗传参数的有效推断。为了研究MTA和不同研究设计的功效，在严格的群体遗传和表型模型下进行了广泛的模拟。它表明，有很大的好处，分析选定的样品中的次要表型。特别是，使用病例对照样本和具有极端主要表型的样本可能比分析同等大小的随机样本更强大。基于序列的关联研究的一个主要挑战是大多数数据集的大小不足以提供足够的功效。通过应用MTA，可以联合分析在不同机制下确定的数据集或针对不同主要性状的数据集，以绘制共同的表型，并大大增加功率。可以使用公共存储库（例如dbGaP）中免费提供的数据集进行组合分析。总之，MTA将在解剖复杂性状的病因学方面发挥重要作用。
For most complex trait association studies using next-generation sequencing, in addition to the primary phenotype of interest, many clinically important secondary traits are also available, which can be analyzed to map susceptibility genes. Owing to high sequencing costs, most studies use selected samples, and the sampling mechanisms of these studies can be complicated. When the primary and secondary traits are correlated, analyses of secondary phenotypes can cause spurious associations in selected samples and existing methods are inadequate to adjust for them. To address this problem, a likelihood-based method, MULTI-TRAIT-ASSOCIATION (MTA) was developed. MTA is flexible and can be applied to any study with known sampling mechanisms. It also allows efficient inferences of genetic parameters. To investigate the power of MTA and different study designs, extensive simulations were performed under rigorous population genetic and phenotypic models. It is demonstrated that there are great benefits for analyzing secondary phenotypes in selected samples. In particular, using case–control samples and samples with extreme primary phenotypes can be more powerful than analyzing random samples of equivalent size. One major challenge for sequence-based association studies is that most data sets are not of sufficient size to be adequately powered. By applying MTA, data sets ascertained under distinct mechanisms or targeted at different primary traits can be jointly analyzed to map common phenotypes and greatly increase power. The combined analysis can be performed using freely available data sets from public repositories, for example, dbGaP. In conclusion, MTA will have an important role in dissecting the etiology of complex traits.