Is Aquatic Life Correlated with an Increased Hematocrit in Snakes?

Background: Physiological adaptations that allow air-breathing vertebrates to remain underwater for long periods mainly involve modifications of the respiratory system, essentially through increased oxygen reserves. Physiological constraints on dive duration tend to be less critical for ectotherms than for endotherms because the former have lower mass-specific metabolic rates. Moreover, comparative studies between marine and terrestrial ectotherms have yet to show overall distinct physiological differences specifically associated with oxygen reserves. Methodology/Principal Findings: We used phylogenetically informed statistical models to test if habitat affects hematocrit (an indicator of blood oxygen stores) in snakes, a lineage that varies widely in habitat use. Our results indicate that both phylogenetic position (clade) and especially habitat are significant predictors of hematocrit. Our analysis also confirms the peculiar respiratory physiology of the marine Acrochordus granulatus. Conclusion/Significance: Contrary to previous findings, marine snakes have significantly–albeit slightly–elevated hematocrit, which should facilitate increased aerobic dive times. Longer dives could have consequences for foraging, mate searching, and predation risks. Alternatively, but not exclusively, increased Hct in marine species might also help t

Hierarchical Generalized Linear Models for Multiple Groups of Rare and Common Variants: Jointly Estimating Group and Individual-Variant Effects

Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/)

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression.

PURPOSE OF REVIEW: The inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the "bad actors" most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures. RECENT FINDINGS: One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information. In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear