Adaptive combination of P-values for family-based association testing with sequence data.
Adaptive combination of P-values for family-based association testing with sequence data.
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DOI：
10.1371/journal.pone.0115971
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lin WY
中科院分区：
文献类型：
--
作者：
Lin WY
基于家庭的研究设计将在识别罕见因果变异方面发挥关键作用，因为罕见因果变异可以在有多个受影响受试者的家庭中丰富。此外，与基于人群的研究不同，家庭研究对于人口亚结构引起的偏差具有稳健性。众所周知，罕见的因果变异很难通过单基因座测试检测出来。因此，通过组合染色体区域或功能单元中的多个变异的信号，已经开发了负荷测试和非负荷测试。这不可避免地将一些中性变体纳入检验统计中，这会削弱统计方法的力量。为了防止中性变量引起的噪音，我们在这里提出了一种“P值自适应组合方法”（缩写为“ADA”）。此方法结合了更可能具有因果关系的变体的每个位点 P 值。具有大 P 值的变体（更有可能是中性变体）从组合统计中被丢弃。除了进行广泛的模拟研究之外，我们还将这些测试应用于遗传分析研讨会 17 数据集，其中根据 1000 基因组计划生成真实序列数据。与一些现有方法相比，ADA 对于中性变体的包含更加稳健。这是一个优点，尤其是在分析二分性状时。然而，ADA 也有一些限制。首先，它的计算量更大。其次，排列过程需要谱系结构和创始人的序列数据。第三，不能包含不相关的控制。我们在此表明，对于基于家庭的研究，ADA 的应用仅限于具有完整谱系信息的二分性状分析。
Family-based study design will play a key role in identifying rare causal variants, because rare causal variants can be enriched in families with multiple affected subjects. Furthermore, different from population-based studies, family studies are robust to bias induced by population substructure. It is well known that rare causal variants are difficult to detect from single-locus tests. Therefore, burden tests and non-burden tests have been developed, by combining signals of multiple variants in a chromosomal region or a functional unit. This inevitably incorporates some neutral variants into the test statistics, which can dilute the power of statistical methods. To guard against the noise caused by neutral variants, we here propose an ‘adaptive combination of P-values method’ (abbreviated as ‘ADA’). This method combines per-site P-values of variants that are more likely to be causal. Variants with large P-values (which are more likely to be neutral variants) are discarded from the combined statistic. In addition to performing extensive simulation studies, we applied these tests to the Genetic Analysis Workshop 17 data sets, where real sequence data were generated according to the 1000 Genomes Project. Compared with some existing methods, ADA is more robust to the inclusion of neutral variants. This is a merit especially when dichotomous traits are analyzed. However, there are some limitations for ADA. First, it is more computationally intensive. Second, pedigree structures and founders' sequence data are required for the permutation procedure. Third, unrelated controls cannot be included. We here show that, for family-based studies, the application of ADA is limited to dichotomous trait analyses with full pedigree information.
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影响因子：
5.2
作者：
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DOI：
10.1093/bioinformatics/bts568
发表时间：
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期刊：
Bioinformatics (Oxford, England)
影响因子：
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