Center for Human Genome Variation, Duke University, School of Medicine, Durham, North Carolina, United States of America.
PLoS Genet. 2013;9(8):e1003709. doi: 10.1371/journal.pgen.1003709. Epub 2013 Aug 22.
A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.
解读个人基因组的一个核心挑战是确定哪些突变最有可能影响疾病。尽管在评估单个突变的功能影响方面已经取得了进展,但那些突变所在基因的特征在很大程度上仍未得到探索。例如,在健康个体中已知携带少数常见功能变体的基因可能被判断为比已知携带许多此类变体的基因更有可能导致某些类型的疾病。然而,到目前为止,还不可能在全基因组范围内对基因耐受功能遗传变异的程度进行定量评估。在这里,我们描述了一项利用 NHLBI 外显子组测序计划 (ESP) 提供的 6503 个全外显子序列的序列数据的努力。具体来说,我们开发了一种不耐受评分系统,该系统评估基因的功能遗传变异是否比基于基因中发现的明显中性变异所预期的更多或更少。为了说明这种不耐受评分的实用性,我们表明,导致孟德尔疾病的基因比不引起任何已知疾病的基因对功能遗传变异的耐受性明显更高,但导致不同类型遗传疾病的基因之间的不耐受性存在显著差异。最后,我们通过展示使用不耐受性排序系统可以帮助解释个人基因组并识别致病性突变来结束讨论。