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精神分裂症中的基因拷贝数变异

Gene copy number variation in schizophrenia.

作者信息

Sutrala Smitha R, Goossens Dirk, Williams Nigel M, Heyrman Lien, Adolfsson Rolf, Norton Nadine, Buckland Paul R, Del-Favero Jurgen

机构信息

Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, CF14 4XN, UK.

出版信息

Schizophr Res. 2007 Nov;96(1-3):93-9. doi: 10.1016/j.schres.2007.07.029. Epub 2007 Sep 7.

Abstract

The possibility that gene copy number variations play a role in the development of complex disorders is a topic of considerable interest. Recent reports have highlighted the large number of such variations that exist and that their occurrence varies considerably between populations. A recent report has suggested that copy number variations in four genes (GRIK3, EFNA5, AKAP5 and CACNG2) may be associated with schizophrenia. One problem with this area of study is the validation of high throughput methods such as comparative genomic hybridisation, as the latter inevitably generates false positives. We have used two contrasting methodologies to determine the validity of the findings reported above which if true would have major implications for the pathogenesis of schizophrenia. Samples from a UK population were tested using a method of allele quantification by DNA pooling and samples from Belgium and northern Sweden were tested using Multiplex Amplicon Quantification (MAQ). Both methods were used to test DNA samples used in the original investigation. No copy number variations were found for any of the genes in any samples. Our data suggests that more reliable methods need to be used to validate the existence of CNVs before full scale association studies are carried out.

摘要

基因拷贝数变异在复杂疾病发生发展中发挥作用的可能性是一个备受关注的话题。近期报告强调了此类变异的数量众多,且其在不同人群中的发生率差异很大。最近一份报告表明,四个基因(GRIK3、EFNA5、AKAP5和CACNG2)的拷贝数变异可能与精神分裂症有关。该研究领域的一个问题是高通量方法(如比较基因组杂交)的验证,因为后者不可避免地会产生假阳性结果。我们使用了两种截然不同的方法来确定上述发现的有效性,这些发现若属实,将对精神分裂症的发病机制产生重大影响。来自英国人群的样本采用DNA池化等位基因定量方法进行检测,来自比利时和瑞典北部的样本采用多重扩增子定量(MAQ)方法进行检测。两种方法均用于检测原始研究中使用的DNA样本。在任何样本中,任何基因均未发现拷贝数变异。我们的数据表明,在进行全面的关联研究之前,需要使用更可靠的方法来验证CNV的存在。

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