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Pulse pressure, wide [keywords]
- Directly measuring spinal cord blood flow and spinal cord perfusion pressure via the collateral network: Correlations with changes in systemic blood pressure. [Journal Article]
- J Thorac Cardiovasc Surg 2015 Jan; 149(1):360-6.
During thoracoabdominal surgery in which segmental arteries are sacrificed over a large area, blood supply routes from collateral networks have received attention as a means of avoiding spinal cord injury. The aim of this study was to investigate spinal cord blood supply through a collateral network by directly measuring spinal cord blood flow and spinal cord perfusion pressure experimentally.In beagle dogs (n = 8), the thoracoabdominal aorta and segmental arteries L1-L7 were exposed, and a temporary bypass was created for distal perfusion. Next, a laser blood flow meter was placed on the spinal dura mater in the L5 region to measure the spinal cord blood flow. The following were measured simultaneously when the direct blood supply from segmental arteries L2-L7 to the spinal cord was stopped: mean systemic blood pressure, spinal cord perfusion pressure (blood pressure within the aortic clamp site), and spinal cord blood flow supplied via the collateral network. These variables were then investigated for evidence of correlations.Positive correlations were observed between mean systemic blood pressure and spinal cord blood flow during interruption of segmental artery flow both with (r = 0.844, P < .01) and without (r = 0.834, P < .01) distal aortic perfusion. In addition, we observed significant correlations between spinal cord perfusion pressure and spinal cord blood flow with and without distal perfusion (r = 0.803, P < .001 and r = 0.832, P < .01, respectively), and between mean systemic blood pressure and spinal cord perfusion pressure with and without distal perfusion (r = 0.898, P < .001 and r = 0.837, P < .001, respectively). The spinal cord was perfused from the collateral network from outside the interrupted segmental arteries, and high systemic blood pressure (∼1.33-fold higher) was needed to obtain the preclamping spinal cord blood flow, whereas 1.68-fold higher systemic blood pressure was needed when distal perfusion was halted.Spinal cord blood flow is positively correlated with mean systemic blood pressure and spinal cord perfusion pressure under spinal cord ischemia caused by clamping a wide range of segmental arteries. In open and endovascular thoracic and thoracoabdominal surgery, elevating mean systemic blood pressure is a simple and effective means of increasing spinal cord blood flow, and measuring spinal cord perfusion pressure seems to be useful for monitoring perioperative spinal cord blood flow.
- Cohort Profile Update: The Mater-University of Queensland Study of Pregnancy (MUSP). [JOURNAL ARTICLE]
- Int J Epidemiol 2014 Dec 16.
The Mater-University of Queensland Study of Pregnancy (MUSP) and its outcomes began in 1981 with data collected on 7223 pregnant woman-child pairs (6753 mothers, of whom 520 had 2 study children, less 50 who had multiple births). These women, and their children, were initially followed for up to 21 years. Since then there have been additional follow-ups of the mothers (27 years) and their children (30 years). There has also been a substantial increase in the breadth of topics addressed, with the collection of biological samples, the administration of structured clinical assessments of mental health and cognitive capacity, and markers of physical health such as lung function and blood pressure. MUSP was originally developed as a birth cohort study. It has become a longitudinal study of growth, development and ageing with an emphasis on the generational transmission of a wide range of factors impacting on adult health outcomes. We welcome interest in our study; for study background and publications visit [www.social sci ence.uq.edu.au/musp] or contact [firstname.lastname@example.org].
- Gaussian graphical models for phenotypes using pedigree data and exploratory analysis using networks with genetic and nongenetic factors based on Genetic Analysis Workshop 18 data. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S99.
Graphical models are increasingly used in genetic analyses to take into account the complex relationships between genetic and nongenetic factors influencing the phenotypes. We propose a model for determining the network structure of quantitative traits while accounting for the correlated nature of the family-based samples using the kinship coefficient. The Gaussian graphical model of age, systolic blood pressure, diastolic blood pressure, hypertension, blood pressure medication use, and smoking status was derived for three time points using real data. We also explored binary sparse graphical models of single-nucleotide polymorphisms (SNPs), covariates, and quantitative traits for exploratory analysis of the data. We validated the applicability of this method by producing a network graph using 20 causal variants, 21 noncausal variants, and 6 binary and quantitative phenotypes using the simulated data. To improve the model's ability to identify associations between the causal variants and the phenotypes, we intend to conduct follow-up studies investigating how to use the relationships between SNPs and between SNPs and phenotypes when analyzing genome wide association data with multiple phenotypes.
- Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S82.
Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, they are not sufficiently straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a 2-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change of a phenotype, integrating kinship correlation in the analysis. We apply our method to the Genetic Analysis Workshop 18 genome-wide association studies data on chromosome 3 to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our method identifies genetic variants associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful for genetic association studies in related samples using longitudinal design.
- Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S80.
This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.'s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.
- Bivariate linear mixed model analysis to test joint associations of genetic variants on systolic and diastolic blood pressure. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S75.
Genetic variants that predispose adults and the elderly to high blood pressure are largely unknown. We used a bivariate linear mixed model approach to jointly test the associations of common single-nucleotide polymorphisms with systolic and diastolic blood pressure using data from a genome-wide association study consisting of genetic variants from chromosomes 3 and 9 and longitudinal measured phenotypes and environment variables from unrelated individuals of Mexican American ethnicity provided by the Genetic Analysis Workshop 18. Despite the small sample size of a maximum of 131 unrelated subjects, a few single-nucleotide polymorphisms appeared significant at the genome-wide level. Simulated data, which was also provided by Genetic Analysis Workshop 18 organizers, showed higher power of the bivariate approach over univariate analysis to detect the association of a selected single-nucleotide polymorphism with modest effect. This suggests that the bivariate approach to longitudinal data of jointly measured and correlated phenotypes can be a useful strategy to identify candidate single-nucleotide polymorphisms that deserve further investigation.
- A penalized linear mixed model for genomic prediction using pedigree structures. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S67.
Genetic Analysis Workshop 18 provided a platform for evaluating genomic prediction power based on single-nucleotide polymorphisms from single-nucleotide polymorphism array data and sequencing data. Also, Genetic Analysis Workshop 18 provided a diverse pedigree structure to be explored in prediction. In this study, we attempted to combine pedigree information with single-nucleotide polymorphism data to predict systolic blood pressure. Our results suggested that the prediction power based on pedigree information only could be unsatisfactory. Using additional information such as single-nucleotide polymorphism genotypes would improve prediction accuracy. In particular, the improvement can be significant when there exist a few single-nucleotide polymorphisms with relatively larger effect sizes. We also compared the prediction performance based on genome-wide association study data (ie, common variants) and sequencing data (ie, common variants plus low-frequency variants). The experimental result showed that inclusion of low frequency variants could not lead to improvement of prediction accuracy.
- Evaluation of estimated genetic values and their application to genome-wide investigation of systolic blood pressure. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S66.
The concept of breeding values, an individual's phenotypic deviation from the population mean as a result of the sum of the average effects of the genes they carry, is of great importance in livestock, aquaculture, and cash crop industries where emphasis is placed on an individual's potential to pass desirable phenotypes on to the next generation. As breeding or genetic values (as referred to here) cannot be measured directly, estimated genetic values (EGVs) are based on an individual's own phenotype, phenotype information from relatives, and, increasingly, genetic data. Because EGVs represent additive genetic variation, calculating EGVs in an extended human pedigree is expected to provide a more refined phenotype for genetic analyses. To test the utility of EGVs in genome-wide association, EGVs were calculated for 847 members of 20 extended Mexican American families based on 100 replicates of simulated systolic blood pressure. Calculations were performed in GAUSS to solve a variation on the standard Best Linear Unbiased Predictor (BLUP) mixed model equation with age, sex, and the first 3 principal components of sample-wide genetic variability as fixed effects and the EGV as a random effect distributed around the relationship matrix. Three methods of calculating kinship were considered: expected kinship from pedigree relationships, empirical kinship from common variants, and empirical kinship from both rare and common variants. Genome-wide association analysis was conducted on simulated phenotypes and EGVs using the additive measured genotype approach in the SOLAR software package. The EGV-based approach showed only minimal improvement in power to detect causative loci.
- Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S62.
Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new methods to identify the gene-environment interactions. In this study, we propose a 2-step approach incorporating an influence measure that capturespure gene-environment effect. We found that pure gene-age interaction has a stronger association than considering the genetic effect alone for systolic blood pressure, measured by counting the number of single-nucleotide polymorphisms (SNPs)reaching a certain significance level. We analyzed the subjects by dividing them into two age groups and found no overlap in the top identified SNPs between them. This suggested that age might have a nonlinear effect on genetic association. Furthermore, the scores of the top SNPs for the two age subgroups were about 3times those obtained when using all subjects for systolic blood pressure. In addition, the scores of the older age subgroup were much higher than those for the younger group. The results suggest that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially age.
- Adjustment of familial relatedness in association test for rare variants. [Journal Article]
- BMC Proc 2014; 8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S39.
High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.