美国博德研究所Michael E. Talkowski等研究人员合作绘制出人类基因组的交叉疾病剂量敏感性图谱。这一研究成果于2022年8月1日在线发表在国际学术期刊《细胞》上。

研究人员量化了整个人类基因组的单倍体不足（即缺失不耐受）和三倍体敏感性（即复制不耐受）的特性。研究人员对来自近一百万人的罕见拷贝数变异（rCNV）进行了协调和元分析，构建了54种疾病的全基因组剂量敏感性目录，其中定义了163个与至少一种疾病相关的剂量敏感性片段。这些片段通常是基因密集型的，并且经常包含显性剂量敏感的驱动基因，研究人员能够使用统计学的精细映射对其进行优先排序。

最后，研究人员设计了一个联合机器学习模型来预测所有常染色体基因的剂量敏感性概率（pHaplo和pTriplo），并确定了2987个单倍体不足的基因和1559个三倍体敏感的基因，包括648个独特的三倍体敏感基因。这一剂量敏感性资源将为人类疾病研究和临床遗传学提供广泛的效用。

据了解，rCNV包括在全球人口中不常出现的缺失和复制，并能带来巨大的疾病风险。

附：英文原文

Title: A cross-disorder dosage sensitivity map of the human genome

Author: Ryan L. Collins, Joseph T. Glessner, Eleonora Porcu, Maarja Lepamets, Rhonda Brandon, Christopher Lauricella, Lide Han, Theodore Morley, Lisa-Marie Niestroj, Jacob Ulirsch, Selin Everett, Daniel P. Howrigan, Philip M. Boone, Jack Fu, Konrad J. Karczewski, Georgios Kellaris, Chelsea Lowther, Diane Lucente, Kiana Mohajeri, Margit Nukas, Xander Nuttle, Kaitlin E. Samocha, Mi Trinh, Farid Ullah, Urmo Vsa, Andres Metspalu, Reedik Mgi, Mari Nelis, Lili Milani, Tnu Esko, Matthew E. Hurles, Swaroop Aradhya, Erica E. Davis, Hilary Finucane, James F. Gusella, Aura Janze, Nicholas Katsanis, Ludmila Matyakhina, Benjamin M. Neale, David Sanders, Stephanie Warren, Jennelle C. Hodge, Dennis Lal, Douglas M. Ruderfer, Jeanne Meck, Reedik Mgi, Tnu Esko, Alexandre Reymond, Zoltán Kutalik, Hakon Hakonarson, Shamil Sunyaev

Issue&Volume: 2022-08-01

Abstract: Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequentlyin the global human population and can confer substantial risk for disease. In thisstudy, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance)and triplosensitivity (i.e., duplication intolerance) throughout the human genome.We harmonized and meta-analyzed rCNVs from nearly one million individuals to constructa genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163dosage sensitive segments associated with at least one disorder. These segments weretypically gene dense and often harbored dominant dosage sensitive driver genes, whichwe were able to prioritize using statistical fine-mapping. Finally, we designed anensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo& pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559triplosensitive genes, including 648 that were uniquely triplosensitive. This dosagesensitivity resource will provide broad utility for human disease research and clinicalgenetics.

DOI: 10.1016/j.cell.2022.06.036

Source: https://www.cell.com/cell/fulltext/S0092-8674(22)00788-7