A general approach to single-nucleotide polymorphism discovery

Gabor T. Marth, Ian Korf, Mark D. Yandell, Raymond T. Yeh, Zhijie Gu, Hamideh Zakeri, Nathan O. Stitziel, La Deana Hillier, Pui Yan Kwok, Warren R. Gish

Research output: Contribution to journalArticlepeer-review

385 Scopus citations


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 2-5). 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.

Original languageEnglish
Pages (from-to)452-456
Number of pages5
JournalNature Genetics
Issue number4
StatePublished - Dec 1999


Dive into the research topics of 'A general approach to single-nucleotide polymorphism discovery'. Together they form a unique fingerprint.

Cite this