The bidirectional association between depressive symptoms, assessed by the HADS, and albuminuria-A longitudinal population-based cohort study with repeated measures from the HUNT2 and HUNT3 Study.

BACKGROUND: Both albuminuria and depression are associated with cardiovascular disease, reflecting low-grade systemic inflammation and endothelial dysfunction. They share risk factors including weight, blood pressure, smoking and blood glucose levels. This longitudinal study aimed to examine bidirectional associations between depression symptoms, indexed by the Hospital Anxiety and Depression scale (HADS), and the inflammation marker albuminuria. METHODS: 2909 persons provided urine samples in both the second (HUNT2, 1995-97) and third wave (HUNT3, 2006-2008) of the Trøndelag Health Survey, Norway. We used a generalized linear regression model (GLM) and ANOVA to assess the association between albuminuria levels (exposure HUNT2) with depression symptoms (outcome in HUNT3); and between depression symptoms (exposure HUNT2) with albuminuria (outcome HUNT3). Depression symptoms were measured with the HADS Depression Scale, analyzed utilising the full 7 items version and analyses restricted to the first 4 items (HADS-D and HADS-4). We accounted for confounders including baseline individual levels of the exposure variables. RESULTS: In this 10-years follow-up study, we found no statistical evidence for an association between baseline depression symptoms and subsequent albuminuria, nor between baseline albuminuria and subsequent depression symptoms. For albuminuria, only 0.04% was explained by prior depression, and for depression, only 0.007% was explained by previous albuminuria levels. The results were essentially the same for the shorter HADS-4 measure. CONCLUSION: There does not appear to be a longitudinal association between albuminuria and depression measured by the HADS.

Is my study system good enough? A case study for identifying maternal effects.

In this paper, we demonstrate how simulation studies can be used to answer questions about identifiability and consequences of omitting effects from a model. The methodology is presented through a case study where identifiability of genetic and/or individual (environmental) maternal effects is explored. Our study system is a wild house sparrow (Passer domesticus) population with known pedigree. We fit pedigree-based (generalized) linear mixed models (animal models), with and without additive genetic and individual maternal effects, and use deviance information criterion (DIC) for choosing between these models. Pedigree and R-code for simulations are available. For this study system, the simulation studies show that only large maternal effects can be identified. The genetic maternal effect (and similar for individual maternal effect) has to be at least half of the total genetic variance to be identified. The consequences of omitting a maternal effect when it is present are explored. Our results indicate that the total (genetic and individual) variance are accounted for. When an individual (environmental) maternal effect is omitted from the model, this only influences the estimated (direct) individual (environmental) variance. When a genetic maternal effect is omitted from the model, both (direct) genetic and (direct) individual variance estimates are overestimated.

Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation.

KEY MESSAGE: A novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented. ABSTRACT: Multi-trait genetic parameter estimation is a relevant topic in animal and plant breeding programs because multi-trait analysis can take into account the genetic correlation between different traits and that significantly improves the accuracy of the genetic parameter estimates. Generally, multi-trait analysis is computationally demanding and requires initial estimates of genetic and residual correlations among the traits, while those are difficult to obtain. In this study, we illustrate how to reparametrize covariance matrices of a multivariate animal model/animal models using modified Cholesky decompositions. This reparametrization-based approach is used in the Integrated Nested Laplace Approximation (INLA) methodology to estimate genetic parameters of multivariate animal model. Immediate benefits are: (1) to avoid difficulties of finding good starting values for analysis which can be a problem, for example in Restricted Maximum Likelihood (REML); (2) Bayesian estimation of (co)variance components using INLA is faster to execute than using Markov Chain Monte Carlo (MCMC) especially when realized relationship matrices are dense. The slight drawback is that priors for covariance matrices are assigned for elements of the Cholesky factor but not directly to the covariance matrix elements as in MCMC. Additionally, we illustrate the concordance of the INLA results with the traditional methods like MCMC and REML approaches. We also present results obtained from simulated data sets with replicates and field data in rice.

Animal models and integrated nested Laplace approximations.

Animal models are generalized linear mixed models used in evolutionary biology and animal breeding to identify the genetic part of traits. Integrated Nested Laplace Approximation (INLA) is a methodology for making fast, nonsampling-based Bayesian inference for hierarchical Gaussian Markov models. In this article, we demonstrate that the INLA methodology can be used for many versions of Bayesian animal models. We analyze animal models for both synthetic case studies and house sparrow (Passer domesticus) population case studies with Gaussian, binomial, and Poisson likelihoods using INLA. Inference results are compared with results using Markov Chain Monte Carlo methods. For model choice we use difference in deviance information criteria (DIC). We suggest and show how to evaluate differences in DIC by comparing them with sampling results from simulation studies. We also introduce an R package, AnimalINLA, for easy and fast inference for Bayesian Animal models using INLA.

Genetic variation and structure of house sparrow populations: is there an island effect?

Population genetic structure and intrapopulation levels of genetic variation have important implications for population dynamics and evolutionary processes. Habitat fragmentation is one of the major threats to biodiversity. It leads to smaller population sizes and reduced gene flow between populations and will thus also affect genetic structure. We use a natural system of island and mainland populations of house sparrows along the coast of Norway to characterize the different population genetic properties of fragmented populations. We genotyped 636 individuals distributed across 14 populations at 15 microsatellite loci. The level of genetic differentiation was estimated using F-statistics and specially designed Mantel tests were conducted to study the influence of population type (i.e. mainland or island) and geographic distance on the genetic population structure. Furthermore, the effects of population type, population size and latitude on the level of genetic variation within populations were examined. Our results suggest that genetic processes on islands and mainland differed in two important ways. First, the intrapopulation level of genetic variation tended to be lower and the occurrence of population bottlenecks more frequent on islands than the mainland. Second, although the general level of genetic differentiation was low to moderate, it was higher between island populations than between mainland populations. However, differentiation increased in mainland populations somewhat faster with geographical distance. These results suggest that population bottleneck events and genetic drift have been more important in shaping the genetic composition of island populations compared with populations on the mainland. Such knowledge is relevant for a better understanding of evolutionary processes and conservation of threatened populations.