Identification of genetic variants that influence circulating IGF1 levels: a targeted search strategy

Hum Mol Genet. 2008 May 15;17(10):1457-64. doi: 10.1093/hmg/ddn034. Epub 2008 Feb 4.

Abstract

An important class of genetic variants that affect disease susceptibility may lie within regulatory elements that influence gene expression. Regulatory sequences are difficult to identify and may be distant from the genes they regulate, but many lie within evolutionarily conserved regions (ECRs). We used comparative genomics to identify 12 ECRs up to 75 kb 5' to and within introns of IGF1. These were screened by high-resolution melting curve analysis, and 18 single-nucleotide polymorphisms (SNPs) were identified, including five novel variants. We analysed two large population-based series of healthy women to test the nine SNPs with minor allele frequency (MAF) >1% within ECRs. Three of the nine SNPs within ECRs (rs35455143, rs35765817 and rs3839984) were significantly associated with circulating IGF1 levels in a multivariate analysis (P <or= 0.02 for each SNP, overall significance P < 0.001). All three are uncommon SNPs (MAF <or= 10%) that lie >70 kb 5' of IGF1. Two (rs35455143 and rs35765817) are in strong LD with each other and appear to have opposite effects on circulating IGF1. Our results on a subset of other SNPs in or near IGF1 were consistent with previously reported associations with IGF1 levels, although only one (rs35767: P = 0.05) was statistically significant. We believe that this is the first systematic study of an association between a phenotype and SNPs within ECRs extending over a large region adjacent to a gene. Targeting ECRs appears to be a useful strategy for identifying a subset of potentially functional non-coding regulatory SNPs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Animals
  • Base Sequence
  • Conserved Sequence
  • Female
  • Genetics, Population
  • Genome, Human
  • Genomics
  • Genotype
  • Humans
  • Insulin-Like Growth Factor I / analysis
  • Insulin-Like Growth Factor I / genetics*
  • Insulin-Like Growth Factor I / metabolism*
  • Middle Aged
  • Polymorphism, Single Nucleotide*
  • Sequence Analysis, DNA

Substances

  • Insulin-Like Growth Factor I