A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism.
A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism.
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DOI：
10.1186/2040-2392-2-1
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发表时间：
2011-01-19
期刊：
影响因子：
6.2
通讯作者：
Pericak-Vance MA
中科院分区：
文献类型：
--
作者：
Hussman JP;Chung RH;Griswold AJ;Jaworski JM;Salyakina D;Ma D;Konidari I;Whitehead PL;Vance JM;Martin ER;Cuccaro ML;Gilbert JR;Haines JL;Pericak-Vance MA
全基因组关联研究已被证明对于疾病易感基因的识别是非常有价值的。然而，由于统计噪声和多重测试造成的假阳性关联，后续研究的候选基因和区域的优先顺序经常被证明是困难的。为了解决这个问题，我们提出了一种新的GWAS降噪(GWAS-NR)方法，作为一种提高在GWAS中检测真实关联的能力的方法，特别是在自闭症等复杂疾病中。GWAS-NR使用线性过滤器来识别在多个数据集中的关联信号之间显示相关性的基因组区域。我们使用计算机模拟来评估GWAS-NR相对于常用的联合分析和Fisher方法检测关联的能力。此外，我们将GWAS-NR应用于来自自闭症遗传资源交换(AGRE)的597个家系的家族性自闭症GWAS和来自自闭症遗传资源交换(AGRE)的第二个现有自闭症家系GWAS，以获得自闭症候选基因概要。通过文献回顾和功能分组对这些基因进行了人工注释和分类，以揭示可能有助于自闭症病因学的生物学途径。计算机模拟表明，与联合分析或Fisher的方法相比，Gwas-NR对真阳性关联信号的分类率要高得多，并且当数据集之间存在不完美的标记重叠时，或者当最接近的疾病相关多态性不是直接分型时，它也能达到这一效果。在两个自闭症数据集中，GWAS-NR分析导致1535个显著连锁不平衡(LD)块重叠431个唯一参考序列(RefSeq)基因。此外，我们确定了最接近非基因重叠LD块的RefSeq基因，产生了860个基因的最终候选集合。对这些相关基因的功能分类表明，它们中的相当大一部分在一条连贯的路径上合作，该路径调节轴突和树突向其适当的突触目标的定向突起。由于统计噪声可能特别影响复杂疾病的研究，在复杂疾病的研究中，遗传异质性或基因之间的相互作用可能会混淆检测关联的能力，GWAS-NR为后续研究提供了一种强大的方法来确定区域的优先顺序。将该方法应用于自闭症数据集，GWAS-NR分析表明，参与轴突和树突的生长和引导的大基因子集与自闭症的病因学有关。
Genome-wide Association Studies (GWAS) have proved invaluable for the identification of disease susceptibility genes. However, the prioritization of candidate genes and regions for follow-up studies often proves difficult due to false-positive associations caused by statistical noise and multiple-testing. In order to address this issue, we propose the novel GWAS noise reduction (GWAS-NR) method as a way to increase the power to detect true associations in GWAS, particularly in complex diseases such as autism. GWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher's methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology. Computer simulations indicate that GWAS-NR achieves a significantly higher classification rate for true positive association signals than either the joint analysis or Fisher's methods and that it can also achieve this when there is imperfect marker overlap across datasets or when the closest disease-related polymorphism is not directly typed. In two autism datasets, GWAS-NR analysis resulted in 1535 significant linkage disequilibrium (LD) blocks overlapping 431 unique reference sequencing (RefSeq) genes. Moreover, we identified the nearest RefSeq gene to the non-gene overlapping LD blocks, producing a final candidate set of 860 genes. Functional categorization of these implicated genes indicates that a significant proportion of them cooperate in a coherent pathway that regulates the directional protrusion of axons and dendrites to their appropriate synaptic targets. As statistical noise is likely to particularly affect studies of complex disorders, where genetic heterogeneity or interaction between genes may confound the ability to detect association, GWAS-NR offers a powerful method for prioritizing regions for follow-up studies. Applying this method to autism datasets, GWAS-NR analysis indicates that a large subset of genes involved in the outgrowth and guidance of axons and dendrites is implicated in the aetiology of autism.
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影响因子：
30.8
作者：
Durand, Christelle M.;Betancur, Catalina;Bourgeron, Thomas
通讯作者：
Bourgeron, Thomas
DOI：
10.1098/rstb.2008.0331
发表时间：
2009-05-27
影响因子：
6.3
作者：
Casanova, Manuel F.;Trippe, Juan
通讯作者：
Trippe, Juan
影响因子：
9.9
作者：
Casanova, MF;Buxhoeveden, DP;Roy, E
通讯作者：
Roy, E
影响因子：
5.3
作者：
Andrews, Melissa R.;Czvitkovich, Stefan;Fawcett, James W.
通讯作者：
Fawcett, James W.
影响因子：
6.9
作者：
BAILEY, A;LECOUTEUR, A;RUTTER, M
通讯作者：
RUTTER, M