Factors Associated with Mutations: Their Matching Rates to Cardiovascular and Neurological Diseases.
Hannah B LucasIan McKnightRegan RainesAbdullah HijaziChristoph HartChan LeeDo-Gyoon KimWei LiPeter H U LeeJoon W ShimPublished in: International journal of molecular sciences (2021)
Monogenic hypertension is rare and caused by genetic mutations, but whether factors associated with mutations are disease-specific remains uncertain. Given two factors associated with high mutation rates, we tested how many previously known genes match with (i) proximity to telomeres or (ii) high adenine and thymine content in cardiovascular diseases (CVDs) related to vascular stiffening. We extracted genomic information using a genome data viewer. In human chromosomes, 64 of 79 genetic loci involving >25 rare mutations and single nucleotide polymorphisms satisfied (i) or (ii), resulting in an 81% matching rate. However, this high matching rate was no longer observed as we checked the two factors in genes associated with essential hypertension (EH), thoracic aortic aneurysm (TAA), and congenital heart disease (CHD), resulting in matching rates of 53%, 70%, and 75%, respectively. A matching of telomere proximity or high adenine and thymine content projects the list of loci involving rare mutations of monogenic hypertension better than those of other CVDs, likely due to adoption of rigorous criteria for true-positive signals. Our data suggest that the factor-disease matching rate is an accurate tool that can explain deleterious mutations of monogenic hypertension at a >80% match-unlike the relatively lower matching rates found in human genes of EH, TAA, CHD, and familial Parkinson's disease.
Keyphrases
- genome wide
- blood pressure
- congenital heart disease
- endothelial cells
- electronic health record
- cardiovascular disease
- dna methylation
- copy number
- aortic aneurysm
- healthcare
- coronary artery disease
- induced pluripotent stem cells
- big data
- brain injury
- high resolution
- spinal cord injury
- deep learning
- mass spectrometry
- subarachnoid hemorrhage
- early onset
- health information
- artificial intelligence
- bioinformatics analysis