Single-nucleotide polymorphisms in the public domain: how useful are they?

There is a concerted effort by a number of public and private groups to identify a large set of human single-nucleotide polymorphisms (SNPs). As of March 2001, 2.84 million SNPs have been deposited in the public database, dbSNP, at the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/SNP/). The 2.84 million SNPs can be grouped into 1.65 million non-redundant SNPs. As part of the International SNP Map Working Group, we recently published a high-density SNP map of the human genome consisting of 1.42 million SNPs (ref. 3). In addition, numerous SNPs are maintained in proprietary databases. Our survey of more than 1,200 SNPs indicates that more than 80% of TSC and Washington University candidate SNPs are polymorphic and that approximately 50% of the candidate SNPs from these two sources are common SNPs (with minor allele frequency of > or =20%) in any given population.     

A general approach to single-nucleotide polymorphism discovery

Single-nucleotide polymorphisms (SNPs) are the most abundant form of human genetic variation and a resource for mapping complex genetic traits. The large volume of data produced by high-throughput sequencing projects is a rich and largely untapped source of SNPs (refs 2, 3, 4, 5). We present here a unified approach to the discovery of variations in genetic sequence data of arbitrary DNA sources. We propose to use the rapidly emerging genomic sequence as a template on which to layer often unmapped, fragmentary sequence data and to use base quality values to discern true allelic variations from sequencing errors. By taking advantage of the genomic sequence we are able to use simpler yet more accurate methods for sequence organization: fragment clustering, paralogue identification and multiple alignment. We analyse these sequences with a novel, Bayesian inference engine, POLYBAYES, to calculate the probability that a given site is polymorphic. Rigorous treatment of base quality permits completely automated evaluation of the full length of all sequences, without limitations on alignment depth. We demonstrate this approach by accurate SNP predictions in human ESTs aligned to finished and working-draft quality genomic sequences, a data set representative of the typical challenges of sequence-based SNP discovery.     

A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms

We describe a map of 1.42 million single nucleotide polymorphisms (SNPs) distributed throughout the human genome, providing an average density on available sequence of one SNP every 1.9 kilobases. These SNPs were primarily discovered by two projects: The SNP Consortium and the analysis of clone overlaps by the International Human Genome Sequencing Consortium. The map integrates all publicly available SNPs with described genes and other genomic features. We estimate that 60,000 SNPs fall within exon (coding and untranslated regions), and 85% of exons are within 5 kb of the nearest SNP. Nucleotide diversity varies greatly across the genome, in a manner broadly consistent with a standard population genetic model of human history. This high-density SNP map provides a public resource for defining haplotype variation across the genome, and should help to identify biomedically important genes for diagnosis and therapy.     

Epigenetic stem cell signature in cancer

Embryonic stem cells rely on Polycomb group proteins to reversibly repress genes required for differentiation. We report that stem cell Polycomb group targets are up to 12-fold more likely to have cancer-specific promoter DNA hypermethylation than non-targets, supporting a stem cell origin of cancer in which reversible gene repression is replaced by permanent silencing, locking the cell into a perpetual state of self-renewal and thereby predisposing to subsequent malignant transformation.