A panel model for predicting the diversity of internal temperatures from English dwellings

Using panel methods, a model for predicting daily mean internal temperature demand across a heterogeneous domestic building stock is developed. The model offers an important link that connects building stock models to human behaviour. It represents the first time a panel model has been used to estimate the dynamics of internal temperature demand from the natural daily fluctuations of external temperature combined with important behavioural, socio-demographic and building efficiency variables. The model is able to predict internal temperatures across a heterogeneous building stock to within ~0.71°C at 95% confidence and explain 45% of the variance of internal temperature between dwellings. The model confirms hypothesis from sociology and psychology that habitual behaviours are important drivers of home energy consumption. In addition, the model offers the possibility to quantify take-back (direct rebound effect) owing to increased internal temperatures from the installation of energy efficiency measures. The presence of thermostats or thermostatic radiator valves (TRV) are shown to reduce average internal temperatures, however, the use of an automatic timer is statistically insignificant. The number of occupants, household income and occupant age are all important factors that explain a proportion of internal temperature demand. Households with children or retired occupants are shown to have higher average internal temperatures than households who do not. As expected, building typology, building age, roof insulation thickness, wall U-value and the proportion of double glazing all have positive and statistically significant effects on daily mean internal temperature. In summary, the model can be used as a tool to predict internal temperatures or for making statistical inferences. However, its primary contribution offers the ability to calibrate existing building stock models to account for behaviour and socio-demographic effects making it possible to back-out more accurate predictions of domestic energy demand

Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype-phenotype associations, 26 represented phenotypes closely related to previously known genotype-phenotype associations, and 33 represented potentially novel genotype-phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits

Aboveground Biomass Accumulation in a Tropical Wet Forest in Nicaragua Following a Catastrophic Hurricane Disturbance 1

Among their effects on forest structure and carbon dynamics, hurricanes frequently create large-scale canopy gaps that promote secondary growth. To measure the accumulation of aboveground biomass (AGBM) in a hurricane damaged forest, we established permanent plots 4 mo after the landfall of Hurricane Joan on the Atlantic coast of Nicaragua in October 1988. We quantified AGBM accumulation in these plots by correlating diameter measurements to AGBM values using a published allometric regression equation for tropical wet forests. In the first measurement year following the storm, AGBM in hurricane-affected plots was quite variable, ranging from 26 to 153 Mg/ha, with a mean of 78 (±15) Mg/ha. AGBM was substantially lower than in two control plots several kilometers outside the hurricane's path (331 ±15 Mg/ha). Biomass accumulation was slow (5.36 ± 0.74 Mg/ha/yr), relative to previous studies of forest regeneration following another hurricane (Hugo) and agricultural activity. We suggest that large-scale, homogenous canopy damage caused by Hurricane Joan impeded the dispersal and establishment of pioneer trees and led to a secondary forest dominated by late successional species that resprouted and survived the disturbance. With the relatively slow rate of biomass accumulation, any tightening in disturbance interval could reduce the maximum capacity of the living biomass to store carbon.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73646/1/j.1744-7429.2005.00077.x.pd

VKORC1 Common Variation and Bone Mineral Density in the Third National Health and Nutrition Examination Survey

Osteoporosis, defined by low bone mineral density (BMD), is common among postmenopausal women. The distribution of BMD varies across populations and is shaped by both environmental and genetic factors. Because the candidate gene vitamin K epoxide reductase complex subunit 1 (VKORC1) generates vitamin K quinone, a cofactor for the gamma-carboxylation of bone-related proteins such as osteocalcin, we hypothesized that VKORC1 genetic variants may be associated with BMD and osteoporosis in the general population. To test this hypothesis, we genotyped six VKORC1 SNPs in 7,159 individuals from the Third National Health and Nutrition Examination Survey (NHANES III). NHANES III is a nationally representative sample linked to health and lifestyle variables including BMD, which was measured using dual energy x-ray absorptiometry (DEXA) on four regions of the proximal femur. In adjusted models stratified by race/ethnicity and sex, SNPs rs9923231 and rs9934438 were associated with increased BMD (p = 0.039 and 0.024, respectively) while rs8050894 was associated with decreased BMD (p = 0.016) among non-Hispanic black males (n = 619). VKORC1 rs2884737 was associated with decreased BMD among Mexican-American males (n = 795; p = 0.004). We then tested for associations between VKORC1 SNPs and osteoporosis, but the results did not mirror the associations observed between VKORC1 and BMD, possibly due to small numbers of cases. This is the first report of VKORC1 common genetic variation associated with BMD, and one of the few reports available that investigate the genetics of BMD and osteoporosis in diverse populations

Long-Term Secondary Care Costs of Endometrial Cancer: A Prospective Cohort Study Nested within the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

BACKGROUND: There is limited evidence on the costs of Endometrial Cancer (EC) by stage of disease. We estimated the long-term secondary care costs of EC according to stage at diagnosis in an English population-based cohort. METHODS: Women participating in UKCTOCS and diagnosed with EC following enrolment (2001-2005) and prior to 31st Dec 2009 were identified to have EC through multiple sources. Survival was calculated through data linkage to death registry. Costs estimates were derived from hospital records accessed from Hospital Episode Statistics (HES) with additional patient level covariates derived from case notes and patient questionnaires. Missing and censored data was imputed using Multiple Imputation. Regression analysis of cost and survival was undertaken. RESULTS: 491 of 641 women with EC were included. Five year total costs were strongly dependent on stage, ranging from £9,475 (diagnosis at stage IA/IB) to £26,080 (diagnosis at stage III). Stage, grade and BMI were the strongest predictors of costs. The majority of costs for stage I/II EC were incurred in the first six months after diagnosis while for stage III / IV considerable costs accrued after the first six months. CONCLUSIONS: In addition to survival advantages, there are significant cost savings if patients with EC are detected earlier.The analysis underpinning this study was supported with a grant from Cancer Research UK (CRUK Grant No: A16008) awarded to RL (http://www.cancerresearchuk. org/funding-for-researchers). The trial (UKCTOCS) for which the patients in this study form a subgroup was funded by the Medical Research Council, Cancer Research UK, the Department of Health and the Eve Appeal

The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery

The field of phenomics has been investigating network structure among large arrays of phenotypes, and genome-wide association studies (GWAS) have been used to investigate the relationship between genetic variation and single diseases/outcomes. A novel approach has emerged combining both the exploration of phenotypic structure and genotypic variation, known as the phenome-wide association study (PheWAS). The Population Architecture using Genomics and Epidemiology (PAGE) network is a National Human Genome Research Institute (NHGRI)-supported collaboration of four groups accessing eight extensively characterized epidemiologic studies. The primary focus of PAGE is deep characterization of well-replicated GWAS variants and their relationships to various phenotypes and traits in diverse epidemiologic studies that include European Americans, African Americans, Mexican Americans/Hispanics, Asians/Pacific Islanders, and Native Americans. The rich phenotypic resources of PAGE studies provide a unique opportunity for PheWAS as each genotyped variant can be tested for an association with the wide array of phenotypic measurements available within the studies of PAGE, including prevalent and incident status for multiple common clinical conditions and risk factors, as well as clinical parameters and intermediate biomarkers. The results of PheWAS can be used to discover novel relationships between SNPs, phenotypes, and networks of interrelated phenotypes; identify pleiotropy; provide novel mechanistic insights; and foster hypothesis generation. The PAGE network has developed infrastructure to support and perform PheWAS in a high-throughput manner. As implementing the PheWAS approach has presented several challenges, the infrastructure and methodology, as well as insights gained in this project, are presented herein to benefit the larger scientific community