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

Genome-wide analysis of single-nucleotide polymorphisms in human expressed sequences Single-nucleotide polymorphisms (SNPs) have been explored as a high-resolution marker set for accelerating the mapping of disease genes 1,2,3,4,5,6,7,8,9,10,11 . 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 ). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Genetics Springer Journals

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

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References (24)

Publisher
Springer Journals
Copyright
Copyright © 2000 by Nature America Inc.
Subject
Biomedicine; Biomedicine, general; Human Genetics; Cancer Research; Agriculture; Gene Function; Animal Genetics and Genomics
ISSN
1061-4036
eISSN
1546-1718
DOI
10.1038/79981
Publisher site
See Article on Publisher Site

Abstract

Single-nucleotide polymorphisms (SNPs) have been explored as a high-resolution marker set for accelerating the mapping of disease genes 1,2,3,4,5,6,7,8,9,10,11 . 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 ).

Journal

Nature GeneticsSpringer Journals

Published: Oct 1, 2000

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