Annotation and interpretation of loss-of-function polymorphisms in human genomes

描述(由申请人提供)：已知人类蛋白质编码基因的功能丧失(LoF)突变在囊性纤维化和肌营养不良等严重疾病中发挥重要作用，最近还被证明影响1型糖尿病和克罗恩病等复杂疾病的风险。作为1000基因组计划的一部分，我们最近对人类LOF变异进行了迄今为止最大规模的系统调查，该计划证实了LOF变异对人类疾病研究的价值，并确定了检测和解释这些变异的关键挑战。我们建议通过构建稳健、准确的工具来克服这些挑战，这些工具用于LOF变体的注释、表征和高通量基因分型。首先，我们将开发一个集成的信息学管道(Annotation of LoF转录，ALOFT)，用于识别和筛选所有类别的LOF变体，包括单核苷酸替换(SNPs)、插入和缺失。其次，我们将利用来自RNA测序实验和疾病突变数据库的数据来创建更准确的预测模型，以预测基因变异对基因表达和剪接的影响，以及它们导致疾病的可能性。最后，我们将把高空管道和上述预测模型应用于30,000多个人类外显子和基因组测序，作为NIH资助的其他项目的一部分，使用产生的注释作为公共可访问的经验证的LoF变体数据库的基础，DBLoF。我们将使用我们的功能注释来开发加权关联测试，并将其应用于在这些已测序的个体中发现新的疾病风险变异。此外，我们将使用这些样本中确定的LOF变异目录来设计定制的基因分型阵列，以便快速、经济有效地询问人类队列中大多数常见的人类LOF变异，并允许在单独资助的关联研究中评估这些变异的表型效应。这项研究将为发现和表征自然功能丧失变异，并探索它们与人类疾病风险的潜在联系提供强大的工具。

DESCRIPTION (provided by applicant): Loss-of-function (LoF) mutations in human protein-coding genes are known to play a major role in severe diseases such as cystic fibrosis and muscular dystrophy, and have also recently been shown to influence the risk of complex diseases such as type 1diabetes and Crohn's disease. We have recently conducted the largest systematic survey to date of human LoF variants, as part of the 1000 Genomes Project, which has confirmed the value of LoF variants for human disease studies and also identified key chalenges for the detection and interpretation of these variants. We propose to overcome these challenges by constructing robust, accurate tools for the annotation, characterization and high-throughput genotyping of LoF variants. Firstly, we will develop an integrated informatic pipeline (Annotation of LoF Transcripts, ALoFT) for the identification and filtering of all classes of LoF variant, including single nucleotide substitutions (SNPs), insertions and deletions. Secondly, we will exploit data from RNA sequencing experiments and disease mutation databases to create more accurate predictive models of the efects of genetic variants on gene expression and splicing, and of their probability of disease causation. Finally, we will apply the ALoFT pipeline and the predictive models described above to over 30,000 human exomes and genomes sequenced as part of other NIH-funded projects, using the resulting annotation as the basis for a publicly accessible database of validated LoF variants, dbLoF. We will use our functional annotation to develop a weighted association test and apply this to the discovery of novel disease risk variants in these sequenced individuals. In addition, we will use the catalogue of LoF variants identified in these samples to design a custom genotyping array permitting rapid, cost-effective interrogation of the majority of common human LoF variants in human cohorts, allowing the phenotypic effects of these variants to be assessed in separately funded association studies. This study will provide powerful tools for discovering and characterizing natural loss-of-function variants, and for exploring their potential association with human disease risk.