Uncovering the Genetic Landscape for Multiple Sleep-Wake Traits

Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior

Use of System Dynamics to Understand Long-Term Impact of Climate Change on Pavement Performance and Maintenance Cost

An increase in the vulnerability of the nation\u27s roadway network to changes in climatic conditions has become an issue of significant concern. Heavy rainfall and high temperatures are examples of climatic factors that can affect pavement performance and conditions in a detrimental way. Some of these effects, specifically on costs associated with constructing and maintaining roads, may not be significant in the short term but may become significant in the long term. The objective of this study was to present a framework to use system dynamics to understand the long-term impact of climate change on pavement performance and maintenance activity. A system dynamics model was created with available pavement performance and climate change data, and the effects on various key factors, such as pavement life and maintenance cost, were evaluated through simulation. Preliminary results show that the long-term effects of changes in air temperature, rainfall, seawater–level rise, and number of hurricanes on pavement performance are significant and that costs are expected to increase significantly (\u3e160% in 100 years) and nonlinearly. From these findings, recommendations are made for obtaining more accurate and reliable data on relevant climate change factors and for using a system dynamics approach to integrate the multi-disciplinary topics of climate change, pavement design and performance, and economics into comprehensive studies