Parameters in Dynamic Models of Complex Traits are Containers of Missing Heritability

Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

<p>Abstract</p> <p>Background</p> <p>Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy <it>C</it>-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.</p> <p>Results</p> <p>Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.</p> <p>Conclusions</p> <p>HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.</p

A systematic review of maternal smoking during pregnancy and fetal measurements with meta-analysis

Funding: The study was supported by the European Cooperation in Science and Technology, who provided funds for publication. KMG is supported by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and by the European Union's Seventh Framework Programme (FP7/2007-2013), projects Early Nutrition and ODIN under grant agreement numbers 289346 and 613977.Peer reviewedPublisher PD

A systematic review of maternal smoking during pregnancy and fetal measurements with meta-analysis

Background Maternal smoking during pregnancy is linked to reduced birth weight but the gestation at onset of this relationship is not certain. We present a systematic review of the literature describing associations between maternal smoking during pregnancy and ultrasound measurements of fetal size, together with an accompanying meta-analysis. Methods Studies were selected from electronic databases (OVID, EMBASE and Google Scholar) that examined associations between maternal smoking or smoke exposure and antenatal fetal ultrasound measurements. Outcome measures were first, second or third trimester fetal measurements. Results There were 284 abstracts identified, 16 papers were included in the review and the metaanalysis included data from eight populations. Maternal smoking was associated with reduced second trimester head size (mean reduction 0.09 standard deviation (SD) [95% CI 0.01, 0.16]) and femur length (0.06 [0.01, 0.10]) and reduced third trimester head size (0.18SD [0.13, 0.23]), femur length (0.27 SD [0.21, 0.32]) and estimated fetal weight (0.18 SD [0.11, 0.24]). Higher maternal cigarette consumption was associated with a lower z score for head size in the second (mean difference 0.09 SD [0, 0.19]) and third (0.15 SD [0.03, 0.26]) trimesters compared to lower consumption. Fetal measurements were not reduced for those whose mothers quit before or after becoming pregnant compared to mothers who had never smoked. Conclusions Maternal smoking during pregnancy is associated with reduced fetal measurements after the first trimester, particularly reduced head size and femur length. These effects may be attenuated if mothers quit or reduce cigarette consumption during pregnancy

Influence of the Environment on Participation in Social Roles for Young Adults with Down Syndrome

Background: The concept of disability is now understood as a result of the interaction between the individual, features related to impairment, and the physical and social environment. It is important to understand these environmental influences and how they affect social participation. The purpose of this study is to describe the social participation of young adults with Down syndrome and examine its relationship with the physical and social environment. Methods: Families ascertained from the Down syndrome ‘Needs Opinion Wishes’ database completed questionnaires during 2011. The questionnaires contained two parts, young person characteristics and family characteristics. Young adults’ social participation was measured using the Assessment of Life Habits (LIFE-H) and the influences of environmental factors were measured by the Measure of the Quality of the Environment (MQE). The analysis involved descriptive statistics and linear and logistic regression. Results: Overall, participation in daily activities was higher (mean 6.45) than in social roles (mean 5.17) (range 0 to 9). When the physical and/or social environment was reported as a facilitator, compared to being no influence or a barrier, participation in social roles was greater (coef 0.89, 95%CI 0.28, 1.52, coef 0.83, 95%CI 0.17, 1.49, respectively). The relationships between participation and both the physical (coef 0.60, 95% CI -0.40, 1.24) and social (coef 0.20, 95%CI -0.47, 0.87) environments were reduced when age, gender, behavior and functioning in ADL were taken into account. Conclusion: We found that young adults’ participation in social roles was influenced more by the physical environment than by the social environment, providing a potentially modifiable avenue for intervention

Observations of the Antarctic ozone hole from 2003-2010

Póster presentado en: EGU General Assembly 2011 celebrada del 3 al 8 de abril en Viena, Austria.The Global Atmosphere Watch of WMO includes several stations in Antarctica that keep a close eye on the ozone layer during the ozone hole season. Observations made during the ozone holes from 2003 to 2010 will be compared to each other and interpreted in light of the meteorological conditions. Satellite observations will be used to get a more general picture of the size and depth of the ozone hole and will also be used to calculate various metrics for ozone hole severity. In 2003, 2005 and 2006, the ozone hole was relatively large with more ozone loss than normal. This is in particular the case for 2006, which by most ozone hole metrics was the most severe ozone holeon record. On the other hand, the ozone holes of 2004, 2007 and 2010 were less severe than normal, and only the very special ozone hole of 2002 had less ozone depletion when one regards the ozone holes of the last decade. The interannual variability will be discussed with the help of meteorological data, such as temperature conditions, possibility for polar stratospheric clouds, vortex shape and vortex longevity. Observations will also be compared to 3-D chemical transport model calculations

The Atlantic salmon genome provides insights into rediploidization

The whole-genome duplication 80 million years ago of the common ancestor of salmonids (salmonid-specific fourth vertebrate whole-genome duplication, Ss4R) provides unique opportunities to learn about the evolutionary fate of a duplicated vertebrate genome in 70 extant lineages. Here we present a high-quality genome assembly for Atlantic salmon (Salmo salar), and show that large genomic reorganizations, coinciding with bursts of transposon-mediated repeat expansions, were crucial for the post-Ss4R rediploidization process. Comparisons of duplicate gene expression patterns across a wide range of tissues with orthologous genes from a pre-Ss4R outgroup unexpectedly demonstrate far more instances of neofunctionalization than subfunctionalization. Surprisingly, we find that genes that were retained as duplicates after the teleost-specific whole-genome duplication 320 million years ago were not more likely to be retained after the Ss4R, and that the duplicate retention was not influenced to a great extent by the nature of the predicted protein interactions of the gene products. Finally, we demonstrate that the Atlantic salmon assembly can serve as a reference sequence for the study of other salmonids for a range of purposes.publishedVersio

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations