An SNP map of the human genome generated by reduced representation shotgun sequencing

Most genomic variation is attributable to single nucleotide polymorphisms (SNPs), which therefore offer the highest resolution for tracking disease genes and population history. It has been proposed that a dense map of 30,000–500,000 SNPs can be used to scan the human genome for haplotypes associated with common diseases. Here we describe a simple but powerful method, called reduced representation shotgun (RRS) sequencing, for creating SNP maps. RRS re-samples specific subsets of the genome from several individuals, and compares the resulting sequences using a highly accurate SNP detection algorithm. The method can be extended by alignment to available genome sequence, increasing the yield of SNPs and providing map positions. These methods are being used by The SNP Consortium, an international collaboration of academic centres, pharmaceutical companies and a private foundation, to discover and release at least 300,000 human SNPs. We have discovered 47,172 human SNPs by RRS, and in total the Consortium has identified 148,459 SNPs. More broadly, RRS facilitates the rapid, inexpensive construction of SNP maps in biomedically and agriculturally important species. SNPs discovered by RRS also offer unique advantages for large-scale genotyping.

[1]  L. Kruglyak Prospects for whole-genome linkage disequilibrium mapping of common disease genes , 1999, Nature Genetics.

[2]  Michael N. Edmonson,et al.  Reliable identification of large numbers of candidate SNPs from public EST data , 1999, Nature Genetics.

[3]  P. Kwok,et al.  Single nucleotide polymorphism hunting in cyberspace , 1998, Human mutation.

[4]  L. Brooks,et al.  A DNA polymorphism discovery resource for research on human genetic variation. , 1998, Genome research.

[5]  Eric Lander,et al.  Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland , 1992, Nature Genetics.

[6]  P. Kwok,et al.  Reading bits of genetic information: methods for single-nucleotide polymorphism analysis. , 1998, Genome research.

[7]  Gabor T. Marth,et al.  A general approach to single-nucleotide polymorphism discovery , 1999, Nature Genetics.

[8]  P. Green,et al.  Base-calling of automated sequencer traces using phred. I. Accuracy assessment. , 1998, Genome research.

[9]  E. Lander The New Genomics: Global Views of Biology , 1996, Science.

[10]  D. Labie,et al.  Molecular Evolution , 1991, Nature.

[11]  N E Morton,et al.  Genetic epidemiology of single-nucleotide polymorphisms. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  J. Weber,et al.  Human whole-genome shotgun sequencing. , 1997, Genome research.

[13]  N. Shen,et al.  Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis , 1999, Nature Genetics.

[14]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[15]  N Risch,et al.  The Future of Genetic Studies of Complex Human Diseases , 1996, Science.

[16]  L Tiret,et al.  Sequence diversity in 36 candidate genes for cardiovascular disorders. , 1999, American journal of human genetics.

[17]  G. D. Wilson,et al.  An SNP map of human chromosome 22 , 2000, Nature.

[18]  Francis S. Collins,et al.  Variations on a Theme: Cataloging Human DNA Sequence Variation , 1997, Science.

[19]  Wen-Hsiung Li,et al.  Low nucleotide diversity in man. , 1991, Genetics.

[20]  P Green,et al.  Base-calling of automated sequencer traces using phred. II. Error probabilities. , 1998, Genome research.

[21]  C. Nusbaum,et al.  Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. , 1998, Science.

[22]  M. Cargill Characterization of single-nucleotide polymorphisms in coding regions of human genes , 1999, Nature Genetics.