摘要
This is an in silico analysis of quantitative trait loci (QTLs), genes, polymorphisms, and chromosomal regions regulating hypertension in the rat genome. Utilizing PGmapper, a program that matches phenoltypes to genes, we identified 266 essential hypertension-associated genes (HyperA), and 83 of these genes contain known hypertension-associated polymorphisms (HyperAP). The majority of HyperAP have been reported in recent years. Surprisingly, only a few of these HyperAP genes have been investigated for their candidacy as the QTL for hypertension. The frequency of candidate genes within peak regions of the QTL is higher than the rest of the QTL region. We also found that QTL located in both gene-rich regions and gene-rich chromosomes contained the most candidate genes. However, the number of candidate genes within a peak region is not associated with the number of total genes in a QTL region. This data could not only facilitate a more rapid and comprehensive identification for the causal genes underlying hypertension in rats, but also provides new insights into genomic structure in regulation of hypertension.
This is an in silico analysis of quantitative trait loci (QTLs), genes, polymorphisms, and chromosomal regions regulating hypertension in the rat genome. Utilizing PGmapper, a program that matches phenoltypes to genes, we identified 266 essential hypertension-associated genes (HyperA), and 83 of these genes contain known hypertension-associated polymorphisms (HyperAP). The majority of HyperAP have been reported in recent years. Surprisingly, only a few of these HyperAP genes have been investigated for their candidacy as the QTL for hypertension. The frequency of candidate genes within peak regions of the QTL is higher than the rest of the QTL region. We also found that QTL located in both gene-rich regions and gene-rich chromosomes contained the most candidate genes. However, the number of candidate genes within a peak region is not associated with the number of total genes in a QTL region. This data could not only facilitate a more rapid and comprehensive identification for the causal genes underlying hypertension in rats, but also provides new insights into genomic structure in regulation of hypertension.
