A Statistical Framework to Guide Sequencing Choices in Pedigrees
A Statistical Framework to Guide Sequencing Choices in Pedigrees
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DOI：
10.1016/j.ajhg.2014.01.005
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发表时间：
2014-02-06
影响因子：
9.8
通讯作者：
Wijsman, Ellen M.
中科院分区：
文献类型：
--
作者：
Cheung, Charles Y. K.;Marchani, Elizabeth;Wijsman, Ellen M.
大谱系的使用是鉴定影响遗传性状的罕见功能变异的有效设计。使用序列数据的成本效益研究可以通过基于谱系的基因型插补来实现，其中一些受试者被测序，并且在其余受试者上推断缺失的基因型。由于高成本，重要的是要仔细优先排序的主题。在这里，我们介绍了一个统计框架，使测序的主题选择之间的系统比较。我们引入了一个度量“局部覆盖”，它允许使用推断的遗传向量来测量基因型插补能力，特别是在一个感兴趣的区域，如一个先前的证据的链接。在缺乏连锁信息的情况下，我们可以使用用系谱结构计算的“全基因组覆盖”度量。这些度量使得能够开发鉴定用于测序的有效选择的方法。如在GIGI-Pick中实施的，该方法还灵活地允许受试者的初始手动选择，并且在仅一些受试者可能可用于测序的约束内优化选择。在本研究中，我们使用模拟来比较GIGI-Pick与PRIMUS，ExomePicks以及选择受试者的常见特设方法。在常见和罕见等位基因的基因型插补中，GIGI-Pick显著优于所有其他方法，并且具有合并先前连锁信息的额外优势。我们还使用了一个真实的家系来证明我们的方法在鉴定因果突变中的实用性。我们的工作使优先排序的主题，以促进解剖的遗传特征的遗传基础。
The use of large pedigrees is an effective design for identifying rare functional variants affecting heritable traits. Cost-effective studies using sequence data can be achieved via pedigree-based genotype imputation in which some subjects are sequenced and missing genotypes are inferred on the remaining subjects. Because of high cost, it is important to carefully prioritize subjects for sequencing. Here, we introduce a statistical framework that enables systematic comparison among subject-selection choices for sequencing. We introduce a metric "local coverage," which allows the use of inferred inheritance vectors to measure genotype-imputation ability specifically in a region of interest, such as one with prior evidence of linkage. In the absence of linkage information, we can instead use a "genome-wide coverage" metric computed with the pedigree structure. These metrics enable the development of a method that identifies efficient selection choices for sequencing. As implemented in GIGI-Pick, this method also flexibly allows initial manual selection of subjects and optimizes selections within the constraint that only some subjects might be available for sequencing. In the present study, we used simulations to compare GIGI-Pick with PRIMUS, ExomePicks, and common ad hoc methods of selecting subjects. In genotype imputation of both common and rare alleles, GIGI-Pick substantially outperformed all other methods considered and had the added advantage of incorporating prior linkage information. We also used a real pedigree to demonstrate the utility of our approach in identifying causal mutations. Our work enables prioritization of subjects for sequencing to facilitate dissection of the genetic basis of heritable traits.