Identification of candidate genes for age-related blood pressure traits on chromosome 11q using GeneSniffer, gene expression and SNP association analyses
Unraveling the genetic basis of common complex disorders such as essential hypertension has been complicated by the interaction of environmental influences and genetic heterogeneity. Previously, we identified a locus on chromosome 11q in Mexican Americans of the San Antonio Family Heart Study (SAFHS) showing evidence for linkage to the rate of change in mean arterial blood pressure (LOD = 5.4; p = 0.0000025), the rate of change in diastolic blood pressure (LOD = 3.98; p = 0.00001) and suggestive evidence for linkage to the rate of change in systolic blood pressure (LOD = 2.77; p = 0.00018). Peak evidence for linkage occurred near marker D11S4464 on chromosome 11q24.1, with a 1-LOD score support interval spanning 16 Mb. Further investigation of this 11q region in the SAFHS using single nucleotide polymorphism (SNP) genotyping and association analysis identified significant association between blood pressure traits and a number of SNPs in the region. Whole genome gene expression analysis in lymphocytes from the SAFHS participants quantified the expression of 169 transcripts representing 157 genes out of the 221 genes (71%) in the 16Mb region. We identified 18 genes whose expression levels were significantly correlated with blood pressure. We also used GeneSniffer to rank candidate genes in our 16Mb region. GeneSniffer identifies all known and predicted genes within a defined chromosome region and ranks each gene according to the number of “hits” it identifies by searching several scientific databases using a dictionary of terms chosen specifically for the phenotype of interest. Results comparing the three approaches identified 2 genes, FLI1 and EVA1, both of which rank within the top 26 genes as likely blood pressure candidates using GeneSniffer and had expression levels that were negatively associated with blood pressure. We present our findings from GeneSniffer, SNP association and gene expression analyses and from comparing the three approaches.