人类表达序列中单核甘酸多态性的全基因组分析。

Genome-wide analysis of single-nucleotide polymorphisms in human expressed sequences.

作者信息

Irizarry K, Kustanovich V, Li C, Brown N, Nelson S, Wong W, Lee C J

机构信息

Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, California, USA.

出版信息

Nat Genet. 2000 Oct;26(2):233-6. doi: 10.1038/79981.

DOI:10.1038/79981

PMID:11017085

Abstract

Single-nucleotide polymorphisms (SNPs) have been explored as a high-resolution marker set for accelerating the mapping of disease genes. Here we report 48,196 candidate SNPs detected by statistical analysis of human expressed sequence tags (ESTs), associated primarily with coding regions of genes. We used Bayesian inference to weigh evidence for true polymorphism versus sequencing error, misalignment or ambiguity, misclustering or chimaeric EST sequences, assessing data such as raw chromatogram height, sharpness, overlap and spacing, sequencing error rates, context-sensitivity and cDNA library origin. Three separate validations-comparison with 54 genes screened for SNPs independently, verification of HLA-A polymorphisms and restriction fragment length polymorphism (RFLP) testing-verified 70%, 89% and 71% of our predicted SNPs, respectively. Our method detects tenfold more true HLA-A SNPs than previous analyses of the EST data. We found SNPs in a large fraction of known disease genes, including some disease-causing mutations (for example, the HbS sickle-cell mutation). Our comprehensive analysis of human coding region polymorphism provides a public resource for mapping of disease genes (available at http://www.bioinformatics.ucla.edu/snp).

摘要

单核苷酸多态性（SNPs）已被探索作为一种高分辨率标记集，用于加速疾病基因的定位。在此，我们报告了通过对人类表达序列标签（ESTs）进行统计分析检测到的48196个候选SNPs，这些SNPs主要与基因的编码区域相关。我们使用贝叶斯推理来权衡真实多态性与测序错误、比对错误或模糊性、错误聚类或嵌合EST序列的证据，评估诸如原始色谱图高度、清晰度、重叠和间距、测序错误率、上下文敏感性和cDNA文库来源等数据。三次独立验证——与54个独立筛选SNPs的基因进行比较、HLA - A多态性验证和限制性片段长度多态性（RFLP）测试——分别验证了我们预测的SNPs的70%、89%和71%。我们的方法检测到的真实HLA - A SNPs比之前对EST数据的分析多十倍。我们在很大一部分已知疾病基因中发现了SNPs，包括一些致病突变（例如，HbS镰状细胞突变）。我们对人类编码区多态性的全面分析为疾病基因定位提供了一个公共资源（可在http://www.bioinformatics.ucla.edu/snp获取）。