基因君官网
2015年12月29日讯 /生物谷BIOON/ –受大众舆论引导，对于基因突变，人们总是谈之色变。而事实上，人体中的绝大部分基因突变是无害，纵使在遗传性疾病中，其致病因也仅表现为1-2种基因的突变而已。因此，如何有的放矢地开展致病因研究，如何区分有害和无害基因突变，一直是科学家们面临的严峻挑战。
最近，来自洛克菲勒大学人类传染病遗传学St. Giles实验室的科学家发明了一种新型基因筛查工具Gene Damage Index，用于预测某种人类基因发生致病基因突变的可能性，以辅助研究人员从大量基因信息中筛除与致病因无关的突变。若无此辅助工具，筛查工作难度堪比大海捞针，且结果容易偏离科学真相。目前，该筛查工具的详细介绍已刊登在《美国国家科学院院刊》（Proceedings of the National Academy of Sciences）杂志中。
Gene Damage Index主要考察某种基因在人类基因库中的突变率，或者“累积突变损伤”，也可通过计算某种基因在特定疾病范畴，如孟德尔疾病、肿瘤、自闭症和原发性免疫缺陷病中的贡献值来衡量其致病意义。
通过基因组分析，研究者曾发现58%的罕见遗传变异仅在2%人类基因中检测出。St. Giles实验室研究团队运用这款工具在健康人体和遗传病患者中检测出某些高突变率基因，据此他们推论这些高突变率基因并非遗传疾病或罕见病真正致病因。
该研究作者Yuval Itan表示，利用Gene Damage Index筛除工具，我们将能有效剔除60%非相关性基因变异，帮助研究者们从下一代浩瀚基因序列中更快速有效的辨识出遗传疾病相关的基因突变。（基因宝jiyinbao.com）
原始出处：
The human gene damage index as a gene-level approach to prioritizing exome variants
The protein-coding exome of a patient with a monogenic disease contains about 20，000 variants， only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation， we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end， we derived the gene damage index (GDI)： a genome-wide， gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure， protein complexity， coding sequence length， and the number of paralogs. We compared GDI with the leading gene-level approaches， genic intolerance， and de novo excess， and demonstrated that GDI performed best for the detection of false positives (i.e.， removing exome variants in genes irrelevant to disease)， whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e.， assessing de novo mutations in genes likely to be disease causing). The GDI server， data， and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.