DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease.
DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease.
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DOI：
10.1371/journal.pone.0179364
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Dina C
中科院分区：
文献类型：
--
作者：
Persyn E;Karakachoff M;Le Scouarnec S;Le Clézio C;Campion D;Consortium FE;Schott JJ;Redon R;Bellanger L;Dina C
下一代测序技术使分析罕见变异对复杂疾病的影响成为可能。作为“常见病-常见变异”范式的延伸，罕见变异研究对于更全面地了解人类性状的遗传结构是必要的。对这些罕见变异的关联研究在统计分析方面显示了新的挑战。由于其频率低，罕见的变异必须按组进行测试。然后，这种方法受到一个未知比例的变量可能是中性的事实的阻碍。罕见变异的风险水平可以由其影响决定，也可以由其在蛋白质序列中的位置决定。更一般地，疾病结构的分子机制可能涉及特定的蛋白质结构域或基因间调控区。虽然各种各样的方法正在优化每个单一标志物的功能权重，但很少评估病例和对照之间的变异位置差异。在这里，我们提出了一种名为DoEstRare的测试，其目的是同时检测基因组区域中疾病风险变体和全球等位基因频率差异的集群。该测试估计，对于病例和对照，通过核方法在遗传区域中的变异位置密度，通过等位基因频率的函数加权。我们通过模拟研究以及对真实的数据集的重新分析，将DoEstRare与先前发表的策略进行了比较。基于各种场景下的模拟，DoEstRare是唯一一个在变体聚类或不聚类时在I型错误和功效方面始终表现出最高性能的。DoEstRare还应用于Brugada综合征和早发性阿尔茨海默病数据，并为其他现有测试提供了补充结果。DoEstRare通过整合变异位置信息，为解释疾病易感性提供了新的机会。DoEstRare在一个用户友好的R包中实现。
Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the “common disease-common variant” paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer’s disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.
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影响因子：
4.5
作者：
Liu DJ;Leal SM
通讯作者：
Leal SM
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
影响因子：
3.5
作者：
Le Scouarnec, Solena;Karakachoff, Matilde;Redon, Richard
通讯作者：
Redon, Richard
DOI：
10.1093/bioinformatics/bts568
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fier H;Won S;Prokopenko D;AlChawa T;Ludwig KU;Fimmers R;Silverman EK;Pagano M;Mangold E;Lange C
通讯作者：
Lange C
影响因子：
4.5
作者：
Neale BM;Rivas MA;Voight BF;Altshuler D;Devlin B;Orho-Melander M;Kathiresan S;Purcell SM;Roeder K;Daly MJ
通讯作者：
Daly MJ