HIGH-THROUGHPUT MULTIPLEX SINGLE-NUCLEOTIDE POLYMORPHISM (SNP) ANALYSIS IN GENES INVOLVED IN METHIONINE METABOLISM
Abstract number: P-W-697
Giusti1 B., Sestini1 I., Saracini1 C., Sticchi2 E., Bolli2 P., Evangelisti1 L., Lapini1 I., Prisco1 D., Gensini2 G.F., Abbate1 R.
11Department of Medical and Surgical Critical Care, University of Florence 22S.Maria agli Ulivi Center, Don C. Gnocchi Foundation Onlus IRCCS, Florence, Italy
How-to-cite Giusti B, Sestini I, Saracini C, Sticchi E, Bolli P, Evangelisti L, Lapini I, Prisco D, Gensini GF, Abbate R. HIGH-THROUGHPUT MULTIPLEX SINGLE-NUCLEOTIDE POLYMORPHISM (SNP) ANALYSIS IN GENES INVOLVED IN METHIONINE METABOLISM. J Thromb Haemost 2007; 5 Supplement 2: P-W-697
Abstract
Introduction: Hyperhomocysteinemia is a well known independent marker factor for atherothrombotic diseases. Hyperhomocysteinemia (HHcy) may result from both acquired and genetic influences. Several polymorphisms are suspected to be associated with HHcy, but data are limited and inconsistent due to the lack of robust populations and/or data on the interaction among SNPs. High-throughput genotyping technologies, such as GenomeLab SNPStream (Beckman Coulter), are now available.
Methods: We developed a multiplex PCR-oligonucledotide extension approach by GenomeLab platform to detect SNPs in genes coding molecules involved in the methionine metabolism. We selected SNPs based on their putative function and frequency in candidate genes. We selected 72 SNPs in AHCY, BHMT, BHMT2, CBS, ENOSF1, FOLH1, MTHFD1, MTHFR, MTR, MTRR, NNMT, PON1, PON2, SLC19A1, SHMT1, TCN2, TYMS genes. They were analyzed in 6 panels of 12 SNPs each according to their nucleotide substitution.
Results: Among the 6 panels, in 1 panel 12/12, 1 panel 10/12, 3 panels 9/12 and 1 panel 8/12 SNPs passed all the steps of validation: therefore, we could analyze 57 SNPs (mean conversion rate 79.2%). As concerns MTHFR C677T and A1298C and MTR A2756G SNPs, we compared data obtained with GenomeLab with those obtained with an electronic microchip technology (Nanogen) and showed a 99.2% concordance. The analyzable SNPs allowed us the haplotype reconstruction to be used in haplotype analysis. Moreover, we performed an analysis of costs of the GenomeLab technology with respect to a low-to-medium throughput technology, Nanogen technology, and a classical manual approach such as RFLP analysis. The cost per SNP of the GenomeLab technology resulted 3.85 and from 4.26 to 9.92 fold less expensive with respect to the Nanogen technology and RFLP analysis, respectively.
Conclusions: The developed approach could represent an useful tool to investigate the genotype-phenotype correlation and the association of these genes with hyperhomocysteinemia and correlated diseases.
