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中性和弱非中性序列变异可能定义个体性。

Neutral and weakly nonneutral sequence variants may define individuality.

机构信息

Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA.

出版信息

Proc Natl Acad Sci U S A. 2013 Aug 27;110(35):14255-60. doi: 10.1073/pnas.1216613110. Epub 2013 Aug 12.

Abstract

Large-scale computational analyses of the growing wealth of genome-variation data consistently tell two distinct stories. The first is expected: coding variants reported in disease-related databases significantly alter the function of affected proteins. The second is surprising: the genomes of healthy individuals appear to carry many variants that are predicted to have some effect on function. As long as the complete experimental analysis of all human genome variants remains impossible, computational methods, such as PolyPhen, SNAP, and SIFT, might provide important insights. These methods capture the effects of particular variants very well and can highlight trends in populations of variants. Diseases are, arguably, extreme phenotypic variations and are often attributable to one or a few severely functionally disruptive variants. Our findings suggest a genomic basis of the different nondisease phenotypes. Prediction methods indicate that variants in seemingly healthy individuals tend to be neutral or weakly disruptive for protein molecular function. These variant effects are predicted to be largely either experimentally undetectable or are not deemed significant enough to be published. This may suggest that nondisease phenotypes arise through combinations of many variants whose effects are weakly nonneutral (damaging or enhancing) to the molecular protein function but fall within the wild-type range of overall physiological function.

摘要

大规模的基因组变异数据计算分析不断讲述着两个截然不同的故事。第一个是意料之中的:在与疾病相关的数据库中报告的编码变异显著改变了受影响蛋白质的功能。第二个则令人惊讶:健康个体的基因组似乎携带了许多被预测对功能有一定影响的变异。只要对所有人类基因组变异的完整实验分析仍然不可能,计算方法(如 PolyPhen、SNAP 和 SIFT)可能会提供重要的见解。这些方法很好地捕捉了特定变异的影响,并可以突出变异群体中的趋势。疾病可以说是极端的表型变异,通常归因于一个或少数几个严重功能失调的变异。我们的发现表明了不同非疾病表型的基因组基础。预测方法表明,看似健康个体中的变异往往对蛋白质分子功能呈中性或弱破坏性。这些变异的影响预计在很大程度上要么是实验上无法检测到的,要么是被认为不够显著而无法发表。这可能表明,非疾病表型是通过许多变异的组合而产生的,这些变异的影响对分子蛋白功能是弱非中性的(有损害或增强作用),但仍在整体生理功能的野生型范围内。

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