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通过GOOD分析法对单核苷酸多态性进行全灵活性基因分型。

Full flexibility genotyping of single nucleotide polymorphisms by the GOOD assay.

作者信息

Sauer S, Lechner D, Berlin K, Plançon C, Heuermann A, Lehrach H, Gut I G

机构信息

Freie Universität Berlin, Fachbereich Biologie, Chemie, Pharmazie, Takustrasse 3, 14195 Berlin-Dahlem, Germany.

出版信息

Nucleic Acids Res. 2000 Dec 1;28(23):E100. doi: 10.1093/nar/28.23.e100.

Abstract

Recently a facile method for genotyping single nucleotide polymorphisms (SNPs) using MALDI mass spectrometry, termed the GOOD assay, was developed. It does not require any purification and is performed with simple liquid handling, thermal incubation and cycling steps. Although this method is well suited to automation and high-throughput analysis of SNPs, it did not allow full flexibility due to lack of certain reagents. A complete set of ss-cyanoethyl phosphoramidites is presented herein that give this SNP genotyping method full sequence and multiplex capabilities. Applications to SNP genotyping in the prion protein gene, the ss-2-adrenergic receptor gene and the angiotensin converting enzyme gene using the GOOD assay are demonstrated. Because SNP genotyping technologies are generally very sensitive to varying DNA quality, the GOOD assay has been stabilised and optimised for low quality DNA. A template extraction method is introduced that allows genotyping from tissue that was taken while placing an ear tag on an animal. This dramatically facilitates the application of genotyping to animal agricultural applications, as it demonstrates that expensive and cumbersome DNA extraction procedures prior to genotyping can be avoided.

摘要

最近,开发了一种利用基质辅助激光解吸电离质谱法(MALDI)对单核苷酸多态性(SNP)进行基因分型的简便方法,称为GOOD分析法。该方法无需任何纯化步骤,通过简单的液体处理、热孵育和循环步骤即可完成。尽管此方法非常适合SNP的自动化和高通量分析,但由于缺乏某些试剂,其灵活性不足。本文介绍了一套完整的单链氰乙基亚磷酰胺,赋予了这种SNP基因分型方法完整的序列和多重分析能力。展示了使用GOOD分析法对朊病毒蛋白基因、β-2肾上腺素能受体基因和血管紧张素转换酶基因中的SNP进行基因分型的应用。由于SNP基因分型技术通常对不同的DNA质量非常敏感,因此已对GOOD分析法进行了优化,以适应低质量DNA。本文还介绍了一种模板提取方法,该方法允许从给动物佩戴耳标的同时采集的组织中进行基因分型。这极大地促进了基因分型在动物农业应用中的应用,因为它表明在基因分型之前可以避免昂贵且繁琐的DNA提取程序。

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