Rare variants create synthetic genome-wide associations.
Rare variants create synthetic genome-wide associations.
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稀有变体会产生整个基因组的关联。
DOI：
10.1371/journal.pbio.1000294
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发表时间：
2010-01-26
期刊：
影响因子：
9.8
通讯作者：
Goldstein DB
中科院分区：
文献类型：
--
作者：
Dickson SP;Wang K;Krantz I;Hakonarson H;Goldstein DB
大量不同的常见变异已被发现与各种常见疾病的风险极轻微增加有关。一项模拟研究表明，对疾病风险有更大影响的罕见变异可能是其中一些关联的原因。
全基因组关联研究（GWAS）现已确定至少2000种与常见疾病或相关性状有关的常见变异（http://www.genome.gov/gwastudies），其中数百种已得到令人信服的重复验证。人们普遍认为，相关标记反映了附近一个常见（次要等位基因频率>0.05）的致病位点的作用，该致病位点与标记相关，这导致了大量的重新测序工作以寻找致病位点。我们提出一种替代解释，即比相关变异罕见得多的变异可能通过随机地更频繁地与常见位点的一个等位基因而非另一个等位基因相关联而产生“合成关联”。尽管合成关联在理论上显然是可能的，但它们从未被作为GWAS发现的一种可能解释而被系统地探究过。在此，我们使用简单的计算机模拟来展示这种合成关联产生的条件以及如何识别它们。我们表明它们不仅是可能的，而且是不可避免的，并且在简单但合理的遗传模型下，它们可能解释或促成全基因组关联研究中报告的许多近期确定的信号。我们还通过展示导致听力损失和镰状细胞贫血的罕见致病突变产生全基因组显著的合成关联来说明合成关联在真实数据集中的行为，在后一种情况下，这种关联延伸超过2.5兆碱基的区间，包含数十个相关变异的“区块”。总之，不常见或罕见的基因变异很容易产生被归因于常见变异的合成关联，在解释和跟进GWAS信号时需要仔细考虑这种可能性。
长期以来人们一直认为，影响较小的常见基因变异对常见人类疾病，如大多数心血管疾病、哮喘和神经精神疾病有重要贡献。使用声称能捕获主要人类群体中90%以上常见变异的技术，针对所有常见疾病的评估常见变异作用的全基因组扫描现已完成。令人惊讶的是，常见变异所解释的变异比例似乎非常小，而且实际确定的变异实例也很少。同时，已发现具有很大影响的罕见变异。现在一项模拟研究表明，即使是那些针对常见变异检测到的信号，原则上也可能来自罕见变异的影响。这对我们理解人类疾病的遗传结构以及设计未来检测致病基因变异的研究具有重要意义。
A large number of different common variants has been associated with very modest increases of risk for various common diseases. A simulation study shows that rare variants with much greater impacts on disease risk may be responsible for some of these associations. Genome-wide association studies (GWAS) have now identified at least 2,000 common variants that appear associated with common diseases or related traits (http://www.genome.gov/gwastudies), hundreds of which have been convincingly replicated. It is generally thought that the associated markers reflect the effect of a nearby common (minor allele frequency >0.05) causal site, which is associated with the marker, leading to extensive resequencing efforts to find causal sites. We propose as an alternative explanation that variants much less common than the associated one may create “synthetic associations” by occurring, stochastically, more often in association with one of the alleles at the common site versus the other allele. Although synthetic associations are an obvious theoretical possibility, they have never been systematically explored as a possible explanation for GWAS findings. Here, we use simple computer simulations to show the conditions under which such synthetic associations will arise and how they may be recognized. We show that they are not only possible, but inevitable, and that under simple but reasonable genetic models, they are likely to account for or contribute to many of the recently identified signals reported in genome-wide association studies. We also illustrate the behavior of synthetic associations in real datasets by showing that rare causal mutations responsible for both hearing loss and sickle cell anemia create genome-wide significant synthetic associations, in the latter case extending over a 2.5-Mb interval encompassing scores of “blocks” of associated variants. In conclusion, uncommon or rare genetic variants can easily create synthetic associations that are credited to common variants, and this possibility requires careful consideration in the interpretation and follow up of GWAS signals. It has long been assumed that common genetic variants of modest effect make an important contribution to common human diseases, such as most forms of cardiovascular disease, asthma, and neuropsychiatric disease. Genome-wide scans evaluating the role of common variation have now been completed for all common disease using technology that claims to capture greater than 90% of common variants in major human populations. Surprisingly, the proportion of variation explained by common variation appears to be very modest, and moreover, there are very few examples of the actual variant being identified. At the same time, rare variants have been found with very large effects. Now it is demonstrated in a simulation study that even those signals that have been detected for common variants could, in principle, come from the effect of rare ones. This has important implications for our understanding of the genetic architecture of human disease and in the design of future studies to detect causal genetic variants.
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