基因君官网
2015年12月31日 讯 /生物谷BIOON/ –尽管基因突变向来拥有坏名声，但大量的突变或许并不是有害的；甚至是在非常罕见的遗传性障碍中，在成千上万的基因中仅有的一个或两个基因突变实际上也会引发疾病的发生，因此区分有害和有益的突变一直是科学界研究的重点。
来自洛克斐勒大学的科学家近日在Proceedings of the National Academy of Sciences杂志上刊登了其最新的研究成果，文章中他们开发了一种新型工具，旨在帮助预测是否一个既定的人类基因有可能包含有促疾病发生的突变，研究者希望该工具可以帮助他们对大量遗传基因进行筛选来帮助预测人类疾病的发生。
研究者Yuval Itan说道，要大海捞针也得先将海水吸走，而过滤掉多余的“噪音”，想要研究的关键基因就清晰可见了；通过进行基因组分析，研究人员发现58%的罕见基因突变位于2%的人类基因中，而他们开发的名为Gene Damage Index（基因损伤指数）的工具可以帮助推理出，在一般人群中频繁突变的基因或许并不太可能引发遗传性及罕见性的疾病，因为这些突变在健康个体中也会频繁出现。
这种基因损伤指数的工具可以帮助揭示一般人群中出现突变的基因数量，或者是积累的突变损伤，而相应的记录结果则可阐明一个给定基因对特殊疾病群体的重要性，比如癌症、孟德尔病、自闭症及原发性免疫缺陷的患者等。最后研究人员表示，利用这种新型工具就可以剔除掉60%不相关的突变，而基因损伤指数工具也将帮助科学家们更轻松地对新一代测序得出的大量数据进行分析。（基因宝jiyinbao.com）
本文系生物谷原创编译整理，欢迎转载！转载请注明来源并附原文链接。更多资讯请下载生物谷APP.
The human gene damage index as a gene-level approach to prioritizing exome variants
Yuval Itana,1, Lei Shanga, Bertrand Boissona, Etienne Patinb,c, Alexandre Bolzea, Marcela Moncada-Véleza,d, Eric Scotte, Michael J. Ciancanellia, Fabien G. Lafaillea, Janet G. Marklea, Ruben Martinez-Barricartea, Sarah Jill de Jonga, Xiao-Fei Konga, Patrick Nitschkef, Aziz Belkadig,h, Jacinta Bustamantea,g,h,i, Anne Puelg,h, Stéphanie Boisson-Dupuisa,g,h, Peter D. Stensonj, Joseph G. Gleesonk,l,m, David N. Cooperj, Lluis Quintana-Murcib,c,2, Jean-Michel Claverien,2, Shen-Ying Zhanga,g,h,3, Laurent Abela,g,h,3, and Jean-Laurent Casanovaa,g,h,m,o,1,3
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.