After GWAS: searching for genetic risk for schizophrenia and bipolar disorder.
After GWAS: searching for genetic risk for schizophrenia and bipolar disorder.
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DOI：
10.1176/appi.ajp.2010.10091340
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发表时间：
2011-03
期刊：
影响因子：
--
通讯作者：
Liu C
中科院分区：
文献类型：
--
作者：
Gershon ES;Alliey-Rodriguez N;Liu C
十年前，人们普遍预计，常见疾病的遗传基础将通过全基因组关联研究(Gwas)来解决，这是一项大规模研究，其中整个基因组都被遗传标记覆盖。然而，大部分可遗传的差异仍然无法解释。作者考虑了几种可供选择的研究策略。例如，虽然人们一直假设一种常见疾病主要与常见的基因变异有关，但现在看来，多种罕见变异对疾病风险都有很大的影响，而且它们可能累积起来，成为常见疾病风险的重要组成部分。自从发现拷贝数变异(CNV)是人类突变的重要来源并与多种常见疾病相关后，这一想法变得更具吸引力。CNV是由人类基因组中的微插入、微缺失和转座组成的结构基因组变体。有观点认为，许多罕见的CNV是相当大比例常见疾病的可能原因，而罕见的CNV被发现是精神分裂症和自闭症的潜在危险因素。另一种方法是“分析基因组”，即重新分析当前GWAS数据的子集，因为全基因组方法中固有的噪声可能隐藏着有效的关联。最后，技术进步和成本下降可能会使大规模全基因组测序成为可能，从而全面识别所有基因变异。将需要比目前荟萃分析中的10,000名受试者更多的研究小组，但结果可能会解决我们目前在常见疾病中的困境：缺失的遗传性在哪里？
Ten years ago it was widely expected that the genetic basis of common disease would be resolved by genome-wide association studies (GWAS), large-scale studies in which the entire genome is covered by genetic markers. However, the bulk of heritable variance remains unexplained. The authors consider several alternative research strategies. For instance, whereas it has been hypothesized that a common disease is associated primarily with common genetic variants, it is now plausible that multiple rare variants each have a potent effect on disease risk and that they could accumulate to become a substantial component of common disease risk. This idea has become more appealing since the discovery that copy number variants (CNVs) are a substantial source of human mutations and are associated with multiple common diseases. CNVs are structural genomic variants consisting of microinsertions, microdeletions, and transpositions in the human genome. It has been argued that numerous rare CNVs are plausible causes of a substantial proportion of common disease, and rare CNVs have been found to be potent risk factors in schizophrenia and autism. Another approach is to “parse the genome,” i.e., reanalyze subsets of current GWAS data, since the noise inherent in genome-wide approaches may be hiding valid associations. Lastly, technological advances and declining costs may allow large-scale genome-wide sequencing that would comprehensively identify all genetic variants. Study groups even larger than the 10,000 subjects in current meta-analyses would be required, but the outcomes may lead to resolution of our current dilemma in common diseases: Where is the missing heritability?
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