Bioinformatic and experimental tools for identification of single-nucleotide polymorphisms in genes with a potential role for the development of the insulin resistance syndrome.
OBJECTIVE
Genes with a possible role for the development of the insulin resistance syndrome (IRS) were scanned for novel single-nucleotide polymorphisms (SNPs) using bioinformatics.


METHODS
GenBank mRNA sequences were compared to the human EST database using gapped BLAST, software that is available on the internet. Mismatches between the search and the EST sequences indicated potential SNPs. Thirty-two SNPs in 13 genes were randomly chosen for experimental verification. PCR and direct sequencing were used to determine the 'true' SNPs. A random sample of 30 Swedish men with slightly elevated diastolic blood pressure (85-94 mmHg) obtained from a population-based study was selected for the sequencing. After completion of these stages, the potential SNPs were checked against the large and rapidly expanding SNP databases HGBASE and NCBI.


RESULTS
EST searches of 146 genes revealed 106 potential SNPs in 44 genes. Experimental analysis of 32 of these potential SNPs verified two SNPs; endothelin receptor A 1471 G/C (3' UTR) and PAI-1 Trp514Arg from a T/C exchange. These two SNPs were also identified in the NCBI and HGBASE databases together with two polymorphisms that were not experimentally identified in our homogeneous Swedish population. Overall, the HGBASE and NCBI databases contained entries of 22% (23 out of 106) of the SNPs identified through our EST searches.


CONCLUSIONS
In the search for genetic variations causing complex diseases like IRS in homogeneous populations (such as the Swedish one used here), important information can be obtained through bioinformatic searches of human genome databases and experimental verification.
Bennet
A M
AM
Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden. Anna.Bennett@imm.ki.se
Näslund
T I
TI
Morgenstern
R
R
de Faire
U
U
D000328:Adult; D016208:Databases, Factual; D014644:Genetic Variation; D006801:Humans; D007333:Insulin Resistance / Q000235:genetics*; D020407:Internet; D008297:Male; D008875:Middle Aged; D016133:Polymerase Chain Reaction; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D013548:Sweden; D013577:Syndrome