Myocardial Protective Effect of Gas Signal Molecule Hydrogen Sulfide on Cardiovascular Disease

Cardiovascular diseases increase continually in the worldwide scale, and its specific pathogenesis has not been completely clear. The gas signal molecule hydrogen sulfide (H2S) is a new type of neuroactive substance, which plays many biological roles in many systems such as cardiovascular system. In recent years, a lot of research has confirmed H2S has myocardial protective effect on cardiovascular diseases such as atherosclerosis, ischemia-reperfusion injury, hypertension and heart failure. This paper reviews the research status of myocardial protective effect of H2S on cardiovascular diseases

Mixed-effects models for GAW18 longitudinal blood pressure data

In this paper, we propose two mixed-effects models for Genetic Analysis Workshop 18 (GAW18) longitudinal blood pressure data. The first method extends EMMA, an efficient mixed-model association-mapping algorithm. EMMA corrects for population structure and genetic relatedness using a kinship similarity matrix. We replace the kinship similarity matrix in EMMA with an estimated correlation matrix for modeling the dependence structure of repeated measurements. Our second approach is a Bayesian multiple association-mapping algorithm based on a mixed-effects model with a built-in variable selection feature. It models multiple single-nucleotide polymorphisms (SNPs) simultaneously and allows for SNP-SNP interactions and SNP-environment interactions. We applied these two methods to the longitudinal systolic blood pressure (SBP) and diastolic blood pressure (DBP) data from GAW18. The extended EMMA method identified a single SNP on Chr5:75506197 (p-value = 4.67 × 10(−7)) for SBP and three SNPs on Chr3:23715851 (p-value = 9.00 × 10(−8)), Chr 17:54834217 (p-value = 1.98 × 10(−7)), and Chr21:18744081 (p-value = 4.95 × 10(−7)) for DBP. The Bayesian method identified several additional SNPs on Chr1:17876090 (Bayes factor [BF] = 102), Chr3:197469358 (BF = 69), Chr15:87675666 (BF = 43), and Chr19:41642807 (BF = 33) for SBP. Furthermore, for SBP, we found a single SNP on Chr3:197469358 (BF = 69) that has a strong interaction with age. We further evaluated the performances of the proposed methods by simulations