Suppr超能文献

用于基因关联研究的高通量单核苷酸多态性分析。

High-throughput SNP analysis for genetic association studies.

作者信息

Marnellos George

机构信息

Sequenom Inc, Pharmaceuticals Division, 3595 John Hopkins Court, San Diego, CA 92121, USA.

出版信息

Curr Opin Drug Discov Devel. 2003 May;6(3):317-21.

Abstract

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation, and millions of SNPs are now documented. Because of their dense distribution across the genome, SNPs are viewed as ideal markers for large-scale genome-wide association studies to discover genes in common complex diseases, such as cancer. To enable such studies, researchers have constructed appropriate sets of SNP markers, by selecting SNPs that are common in major human populations and by charting the patterns of co-occurrence of SNPs, which could further guide marker selection. High-throughput SNP analysis technologies have also been developed, which can analyze thousands of SNPs in thousands of samples. As SNP analysis techniques and SNP marker sets are improving, researchers have begun to carry out large-scale genome scans for disease genes, with encouraging first results.

摘要

单核苷酸多态性(SNPs)是最常见的基因变异类型,目前已记录有数以百万计的单核苷酸多态性。由于它们在基因组中分布密集,单核苷酸多态性被视为大规模全基因组关联研究的理想标记,用于发现常见复杂疾病(如癌症)中的基因。为了开展此类研究,研究人员通过选择在主要人群中常见的单核苷酸多态性,并绘制单核苷酸多态性的共现模式,构建了合适的单核苷酸多态性标记集,这可以进一步指导标记选择。还开发了高通量单核苷酸多态性分析技术,该技术可以分析数千个样本中的数千个单核苷酸多态性。随着单核苷酸多态性分析技术和单核苷酸多态性标记集的不断改进,研究人员已开始对疾病基因进行大规模基因组扫描,并取得了令人鼓舞的初步成果。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验