Lower rate of genomic variation identified in the trans-membrane domain of monoamine sub-class of human G-protein coupled receptors: The Human GPCR-DB database

Claes Wahlestedt, Anthony J. Brookes, Salim Mottagui-Tabar

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Background: We have surveyed, compiled and annotated nucleotide variations in 338 human 7-transmembrane receptors (G-protein coupled receptors). In a sample of 32 chromosomes from a Nordic population, we attempted to determine the allele frequencies of 80 non-synonymous SNPs, and found 20 novel polymorphic markers. GPCR receptors of physiological and clinical importance were prioritized for statistical analysis. Natural variation and rare mutation information were merged and presented online in the Human GPCR-DB database http://cyrix.cgb.ki.se. Results: The average number of SNPs per 1000 bases of exonic sequence was found to be twice the average number of SNPs per Kilobase of intronic regions (2.2 versus 1.0). Of the 338 genes, 111 were single exon genes, that is, were intronless. The average number of exonic-SNPs per single-exon gene was 3.5 (n = 395) while that for multi-exon genes was 0.8 (n = 1176). The average number of variations within the different protein domain (N-terminus, internal- and external-loops, trans-membrane region, C-terminus) indicates a lower rate of variation in the trans-membrane region of Monoamine GPCRs, as compared to Chemokine- and Peptide-receptor sub-classes of GPCRs. Conclusions: Single-exon GPCRs on average have approximately three times the number of SNPs as compared to GPCRs with introns. Among various functional classes of GPCRs, Monoamine GPRCs have lower number of natural variations within the trans-membrane domain indicating evolutionary selection against non-synonymous changes within the membrane-localizing domain of this sub-class of GPCRs.

Original languageEnglish (US)
Article number91
JournalBMC genomics
Volume5
DOIs
StatePublished - Dec 4 2004

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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