A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis
A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis
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DOI：
10.1371/journal.pone.0213350
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发表时间：
2019-03-27
期刊：
影响因子：
3.7
通讯作者：
Johansson, Erik
中科院分区：
文献类型：
--
作者：
Rentoft, Matilda;Svensson, Daniel;Johansson, Erik
全基因组测序是研究人类常染色体显性遗传病的一种很有前途的方法。然而，通过这种方法观察到的大量遗传变异在试图识别致病变异时构成了挑战。这通常是通过将疾病研究限制在最具破坏性的变体上来处理的。G.那些在编码区发现的，而忽略了其余的遗传变异。这种有偏见的方法部分解释了为什么许多显性遗传疾病家族的遗传原因，尽管被纳入全基因组测序研究，今天仍然没有解决。在这里，我们探索使用地理上匹配的对照人群，以尽量减少候选致病变异的数量，而不排除基于基因组位置或功能预测假设的变异。为了证明地理上匹配的对照人群的益处，我们在一个常染色体显性形式的结直肠癌家族中应用了典型的疾病变异过滤策略。通过使用地理上匹配的对照群体，我们最终在全基因组范围内获得了26个候选变体。这与仅使用可用的公共变体数据集时留下的数万个候选项形成对比。局部控制群体的影响是双重的，它（1）减少了受影响个体之间共享的候选变体的总数，更重要的是（2）增加了候选变体数量减少的速率，因为过滤策略中包括了额外的受影响家族成员。我们证明，地理匹配的对照群体的应用有效地限制了候选致病变异的数量，并可能提供在全基因组范围内识别适合功能研究的变异的方法。
Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e. g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide.