Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance

On estimation and identifiability issues of sex-linked inheritance with a case study of pigmentation in Swiss barn owl (Tyto alba)

Genetic evaluation using animal models or pedigree-based models generally assume only autosomal inheritance. Bayesian animal models provide a flexible framework for genetic evaluation, and we show how the model readily can accommodate situations where the trait of interest is influenced by both autosomal and sex-linked inheritance. This allows for simultaneous calculation of autosomal and sex-chromosomal additive genetic effects. Inferences were performed using integrated nested Laplace approximations (INLA), a nonsampling-based Bayesian inference methodology. We provide a detailed description of how to calculate the inverse of the X- or Z-chromosomal additive genetic relationship matrix, needed for inference. The case study of eumelanic spot diameter in a Swiss barn owl (Tyto alba) population shows that this trait is substantially influenced by variation in genes on the Z-chromosome (sigma(2)(z) = 0.2719 and sigma(2)(a) = 0.4405). Further, a simulation study for this study system shows that the animal model accounting for both autosomal and sex-chromosome-linked inheritance is identifiable, that is, the two effects can be distinguished, and provides accurate inference on the variance components

Streamflow forecast sensitivity to air temperature forecast calibration for 139 Norwegian catchments

In this study, we used meteorological ensemble forecasts as input to hydrological models to quantify the uncertainty in forecasted streamflow, with a particular focus on the effect of temperature forecast calibration on the streamflow ensemble forecast skill. In catchments with seasonal snow cover, snowmelt is an important flood-generating process. Hence, high-quality air temperature data are important to accurately forecast streamflows. The sensitivity of streamflow ensemble forecasts to the calibration of temperature ensemble forecasts was investigated using ensemble forecasts of temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) covering a period of nearly 3 years, from 1 March 2013 to 31 December 2015. To improve the skill and reduce biases of the temperature ensembles, the Norwegian Meteorological Institute (MET Norway) provided parameters for ensemble calibration, derived using a standard quantile mapping method where HIRLAM, a high-resolution regional weather prediction model, was used as reference. A lumped HBV (Hydrologiska Byråns Vattenbalansavdelning) model, distributed on 10 elevation zones, was used to estimate the streamflow. The results show that temperature ensemble calibration affected both temperature and streamflow forecast skill, but differently depending on season and region. We found a close to 1:1 relationship between temperature and streamflow skill change for the spring season, whereas for autumn and winter large temperature skill improvements were not reflected in the streamflow forecasts to the same degree. This can be explained by streamflow being less affected by subzero temperature improvements, which accounted for the biggest temperature biases and corrections during autumn and winter. The skill differs between regions. In particular, there is a cold bias in the forecasted temperature during autumn and winter along the coast, enabling a large improvement by calibration. The forecast skill was partly related to elevation differences and catchment area. Overall, it is evident that temperature forecasts are important for streamflow forecasts in climates with seasonal snow cover.</p

Enterotoxigenic Escherichia coli and Vibrio cholerae Diarrhea, Bangladesh, 2004

Flooding in Dhaka in July 2004 caused epidemics of diarrhea. Enterotoxigenic Escherichia coli (ETEC) was almost as prevalent as Vibrio cholerae O1 in diarrheal stools. ETEC that produced heat-stable enterotoxin alone was most prevalent, and 78% of strains had colonization factors. Like V. cholerae O1, ETEC can cause epidemic diarrhea

Flexible modelling of spatial variation in agricultural field trials with the R package INLA

The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ( AR1⊗AR1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the AR1⊗AR1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA