颜林林
(2022-08-04 23:48):
#paper doi:10.1016/j.cell.2022.06.036 Cell, 2022, A cross-disorder dosage sensitivity map of the human genome. 作为专业背景是生信的我,经常会思考,纯计算的文章究竟能发到多好的杂志上,是否也有机会能刷刷顶刊主刊。或者换个说法,从码农转职而来的、没啥经费支持的研究人员,只凭借一台电脑(及其背后的互联网),是否也可以做出“顶级”生物学研究?之所以有此不自信,主要还是太多来自传统研究学者及其遵循的研究范式所提出的质疑,大家普遍认为“纯计算”本身不可信,总需要有“自己产出的生物数据”才算是可信和有意义的。然而,这篇登上《Cell》杂志的文章,却真是这样一个“纯计算”的案例。固然它是有Harvard和Broad institute的招牌加持,然而,其整合的来自17个数据源的基因组数据,都来自既往其他研究,涉及54种疾病,近百万例入组受试,重新分析并人工核对了罕见CNV突变,以及这些CNV在相应疾病背景下,对它们经由剂量效应而造成的表型影响,进行了评估。文章整合得到的数据,以及相应的分析方法及产出结果,其质量都并不逊色于大多数“直接产出生物数据”的工作。此外,文章的图表(包括补充材料的图表,比如Fig.S3)也都挺赏心悦目的。
A cross-disorder dosage sensitivity map of the human genome
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Abstract:
Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, 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 construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.
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