Rapid gene mapping in Caenorhabditis elegans using a high density polymorphism map.

Single nucleotide polymorphisms (SNPs) are valuable genetic markers of human disease. They also comprise the highest potential density marker set available for mapping experimentally derived mutations in model organisms such as Caenorhabditis elegans. To facilitate the positional cloning of mutations we have identified polymorphisms in CB4856, an isolate from a Hawaiian island that shows a uniformly high density of polymorphisms compared with the reference Bristol N2 strain. Based on 5.4 Mbp of aligned sequences, we predicted 6,222 polymorphisms. Furthermore, 3,457 of these markers modify restriction enzyme recognition sites ('snip-SNPs') and are therefore easily detected as RFLPs. Of these, 493 were experimentally confirmed by restriction digest to produce a snip-SNP map of the worm genome. A mapping strategy using snip-SNPs and bulked segregant analysis (BSA) is outlined. CB4856 is crossed into a mutant strain, and exclusion of CB4856 alleles of a subset of snip-SNPs in mutant progeny is assessed with BSA. The proximity of a linked marker to the mutation is estimated by the relative proportion of each form of the biallelic marker in populations of wildtype and mutant genomes. The usefulness of this approach is illustrated by the rapid mapping of the dyf-5 gene.

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.

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.