Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.
Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.
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DOI：
10.1371/journal.pgen.1003443
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发表时间：
2013-04
期刊：
影响因子：
4.5
通讯作者：
Roeder K
中科院分区：
文献类型：
--
作者：
Liu L;Sabo A;Neale BM;Nagaswamy U;Stevens C;Lim E;Bodea CA;Muzny D;Reid JG;Banks E;Coon H;Depristo M;Dinh H;Fennel T;Flannick J;Gabriel S;Garimella K;Gross S;Hawes A;Lewis L;Makarov V;Maguire J;Newsham I;Poplin R;Ripke S;Shakir K;Samocha KE;Wu Y;Boerwinkle E;Buxbaum JD;Cook EH Jr;Devlin B;Schellenberg GD;Sutcliffe JS;Daly MJ;Gibbs RA;Roeder K
我们报告了1,039名诊断为自闭症谱系障碍（ASD）的受试者和870名从NIMH库中选择的与病例具有相似祖先的对照的全外显子组测序（WES）结果。WES数据来自两个中心，使用不同的方法产生序列并从中调用变体。因此，最初的目标是确保来自不同中心的数据的罕见变异分布相似。这被证明是简单的，通过缺失数据的分数，读取深度和替代参考读取的平衡来过滤调用的变体。使用在两个中心测序的七个样品和关联研究的结果对结果进行评价。接下来，我们讨论了如何将来自各中心的数据和/或结果进行合并。基于基因的关联性分析是一个明显的选择，但是关联性的统计数据应该在不同中心合并（荟萃分析）还是应该合并数据然后进行分析（大分析）？由于许多基于基因的测试的性质，我们通过理论和模拟表明，大分析比元分析具有更好的能力。最后，在分析数据的关联性之前，我们探讨了群体结构对这些数据中罕见变异分析的影响。像其他最近的研究，我们发现的证据表明，人口结构可以混淆病例对照研究的罕见变异的聚集在祖先空间;然而，不像最近的一些研究，对于这些数据，我们发现，基于主成分的分析足以控制祖先，并产生适当的分布检验统计。在使用各种基于基因的测试和荟萃分析和大型分析之后，我们在这个样本中没有发现ASD的新风险基因。我们的研究结果表明，标准的基于基因的测试将需要更大的病例和对照样本才能有效地发现基因，即使是像ASD这样的疾病。本研究评估了两个中心测序的病例和对照样本中罕见变异与自闭症谱系障碍（ASD）的相关性。在进行关联分析之前，我们研究了如何将研究间的信息联合收割机。我们首先在罕见变异的分布方面协调了跨中心的全外显子组序列（WES）数据。主要特征包括通过缺失数据的分数、读段深度和替代读段与参考读段的平衡来过滤所调用的变体。过滤后，来自两个中心测序的七个样本的绝大多数变异呼叫匹配。我们还评估了是否应该将来自每个中心的数据进行联合收割机汇总统计（荟萃分析）或将数据进行联合收割机合并并一起分析（大分析）。对于许多基于基因的测试，我们表明，大型分析产生更大的权力。在对1,039例ASD病例和870例对照的数据进行质量控制和一系列分析后，没有基因显示出显着关联的外显子组证据。我们的研究结果与最近的结果一致，这些结果表明数百个基因影响ASD的风险;他们表明罕见的风险变异分散在这些基因中，因此需要更大的样本来识别这些基因。
We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD. This study evaluates association of rare variants and autism spectrum disorders (ASD) in case and control samples sequenced by two centers. Before doing association analyses, we studied how to combine information across studies. We first harmonized the whole-exome sequence (WES) data, across centers, in terms of the distribution of rare variation. Key features included filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. After filtering, the vast majority of variants calls from seven samples sequenced at both centers matched. We also evaluated whether one should combine summary statistics from data from each center (meta-analysis) or combine data and analyze it together (mega-analysis). For many gene-based tests, we showed that mega-analysis yields more power. After quality control of data from 1,039 ASD cases and 870 controls and a range of analyses, no gene showed exome-wide evidence of significant association. Our results comport with recent results demonstrating that hundreds of genes affect risk for ASD; they suggest that rare risk variants are scattered across these many genes, and thus larger samples will be required to identify those genes.
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影响因子：
16.2
作者：
Iossifov I;Ronemus M;Levy D;Wang Z;Hakker I;Rosenbaum J;Yamrom B;Lee YH;Narzisi G;Leotta A;Kendall J;Grabowska E;Ma B;Marks S;Rodgers L;Stepansky A;Troge J;Andrews P;Bekritsky M;Pradhan K;Ghiban E;Kramer M;Parla J;Demeter R;Fulton LL;Fulton RS;Magrini VJ;Ye K;Darnell JC;Darnell RB;Mardis ER;Wilson RK;Schatz MC;McCombie WR;Wigler M
通讯作者：
Wigler M
影响因子：
30.8
作者：
通讯作者：
--
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
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影响因子：
4.5
作者：
Chahrour MH;Yu TW;Lim ET;Ataman B;Coulter ME;Hill RS;Stevens CR;Schubert CR;ARRA Autism Sequencing Collaboration;Greenberg ME;Gabriel SB;Walsh CA
通讯作者：
Walsh CA
影响因子：
2.1
作者：
Han, Fang;Pan, Wei
通讯作者：
Pan, Wei