Markers of Murine Embryonic and Neural Stem Cells, Neurons and Astrocytes: Reference Points for Developmental Neurotoxicity Testing

Developmental neurotoxicity (DNT) is a serious concern for environmental chemicals, as well as for food and drug constituents. Animal-based DNT models have relatively low sensitivity, and they are limited by high work-load, cost and animal ethics. Murine embryonic stem cells (mESC) recapitulate several critical processes involved in the development of the nervous system if they are induced to differentiate into neural cells. They therefore represent an alternative toxicological model to predict human hazard. In this review, we discuss how mESC can be used for DNT assays. We have compiled a list of mRNA markers that define undifferentiated mESC (n = 42); neural stem cells (n = 73), astrocytes (n = 25) and the pattern of different neuronal and non-neuronal cell types generated (n = 57). We propose that transcriptional profiling can be used as a sensitive endpoint in toxicity assays to distinguish neural differentiation states during normal and disturbed development. Importantly, we believe that it can be scaled up to relatively high throughput whilst still providing rich information on disturbances affecting small cell subpopulations. Moreover, this approach can provide insight into underlying mechanisms and pathways of toxicity. We broadly discuss the methodological basis of marker lists and DNT assay design. The discussion is put in the context of a new generation of alternative assays (embryonic stem cell based DNT testing = ESDNT V2.0), that may later include human induced pluripotent stem cells, and that are not designed for 1:1 replacement of animal experiments, but are rather intended to improve human risk assessment by using independent scientific principles.JRC.I.2-Validation of Alternative Method

Quantitative conservation genetics of the rare plants Scabiosa canescens (Dipsacaceae) and Silene diclinis (Caryophyllaceae)

This thesis concerns quantitative genetic aspects of the conservation biology of two rare plants, Scabiosa canescens and Silene diclinis. Particular attention was given to the effects of genetic drift on the structuring of variation in allozymes and quantitative characters, the association between (current) population size and quantitative genetic variation, the level of inbreeding depression in fitness characters and morphology in a small, isolated population, and the effect of inbreeding and intraspecific hybridization on developmental instability. Some of the Scabiosa studies included the more common and widespread Scabiosa columbaria as a reference species. The quantitative genetic structure within Scabiosa canescens and Silene diclinis — low between-population variance combined with high within-population variation — suggests that the current number of individuals is a poor predictor of the adaptive potential of a population and that it takes many generations before random genetic drift reduces the quantitative genetic variation of small, isolated populations. A majority of the allozyme and quantitative characters in Scabiosa canescens and Silene diclinis showed similar levels of population subdivision, suggesting some overlap between the structure of variation in monogenic and polygenic characters. However, there is no reason to believe that this pattern is general. For example, the Silene data indicated a tendency for some characters (leaf size) to be more strongly differentiated than the allozymes, and S. columbaria, a close relative of S. canescens, showed higher population differentiation for phenotypic traits than for allozymes. Hence, as long as high resolution QTL-analyses cannot be performed, conservation strategies based on variation at marker loci may be misleading. The observation that flower fluctuating asymmetry (FA) is sensitive to inbreeding suggests that measures of developmental instability could serve as an early warning system for monitoring the effects of genetic stress in rare, threatened species. However, given the low statistical power in studies of developmental instability, I urge caution in the use of FA as a measure of genetic stress at the individual level, unless several measurements on repeated organs from each individual are available

Sparse Convolutional Neural Networks for Genome-Wide Prediction

Genome-wide prediction (GWP) has become the state-of-the art method in artificial selection. Data sets often comprise number of genomic markers and individuals in ranges from a few thousands to millions. Hence, computational efficiency is important and various machine learning methods have successfully been used in GWP. Neural networks (NN) and deep learning (DL) are very flexible methods that usually show outstanding prediction properties on complex structured data, but their use in GWP is nevertheless rare and debated. This study describes a powerful NN method for genomic marker data that can easily be extended. It is shown that a one-dimensional convolutional neural network (CNN) can be used to incorporate the ordinal information between markers and, together with pooling and l (1)-norm regularization, provides a sparse and computationally efficient approach for GWP. The method, denoted CNNGWP, is implemented in the deep learning software Keras, and hyper-parameters of the NN are tuned with Bayesian optimization. Model averaged ensemble predictions further reduce prediction error. Evaluations show that CNNGWP improves prediction error by more than 25% on simulated data and around 3% on real pig data compared with results obtained with GBLUP and the LASSO. In conclusion, the CNNGWP provides a promising approach for GWP, but the magnitude of improvement depends on the genetic architecture and the heritability

Approximate Bayesian neural networks in genomic prediction

International audienceAbstractBackgroundGenome-wide marker data are used both in phenotypic genome-wide association studies (GWAS) and genome-wide prediction (GWP). Typically, such studies include high-dimensional data with thousands to millions of single nucleotide polymorphisms (SNPs) recorded in hundreds to a few thousands individuals. Different machine-learning approaches have been used in GWAS and GWP effectively, but the use of neural networks (NN) and deep-learning is still scarce. This study presents a NN model for genomic SNP data.ResultsWe show, using both simulated and real pig data, that regularization is obtained using weight decay and dropout, and results in an approximate Bayesian (ABNN) model that can be used to obtain model averaged posterior predictions. The ABNN model is implemented in mxnet and shown to yield better prediction accuracy than genomic best linear unbiased prediction and Bayesian LASSO. The mean squared error was reduced by at least 6.5% in the simulated data and by at least 1% in the real data. Moreover, by comparing NN of different complexities, our results confirm that a shallow model with one layer, one neuron, one-hot encoding and a linear activation function performs better than more complex models.ConclusionsThe ABNN model provides a computationally efficient approach with good prediction performance and in which the weight components can also provide information on the importance of the SNPs. Hence, ABNN is suitable for both GWP and GWAS

A proximal LAVA method for genome-wide association and prediction of traits with mixed inheritance patterns

Abstract The genetic basis of phenotypic traits is highly variable and usually divided into mono-, oligo- and polygenic inheritance classes. Relatively few traits are known to be monogenic or oligogeneic. The majority of traits are considered to have a polygenic background. To what extent there are mixtures between these classes is unknown. The rapid advancement of genomic techniques makes it possible to directly map large amounts of genomic markers (GWAS) and predict unknown phenotypes (GWP). Most of the multi-marker methods for GWAS and GWP falls into one of two regularization frameworks. The first framework is based on ℓ₁-norm regularization (e.g. the LASSO) and is suitable for mono- and oligogenic traits, whereas the second framework regularize with the ℓ₂-norm (e.g. ridge regression; RR) and thereby is favourable for polygenic traits. A general framework for mixed inheritance is lacking

MOESM1 of Genome-wide prediction using Bayesian additive regression trees

Additional file 1. Construction of regression trees from SNP data. This file describes how to build a regression tree for fictive data of two SNPs and one phenotype, and how to make the genetic interpretation of the resulting response surface

Modeling cow somatic cell count using sensor data as input to generalized additive models

This research paper presents a study investigating if sensor data from an automatic milking rotary could be used to model cow somatic cell count (composite milk SCC: CMSCC). CMSCC is valuable for udder health monitoring and individual cow udder health surveillance could be improved by predicting CMSCC between routine samplings. Data regularly recorded in the automatic milking rotary, in one German dairy herd, were collected for analysis. The cows (Holstein-Friesian,n= 372) were milked twice daily and sampled once weekly in afternoon milkings for 8 weeks for CMSCC. From the potential independent variables, including quarter conductivity, milk flow, blood in milk, kick-offs, not milked quarters and incomplete milkings, new variables that combined quarter data were created. Past period records, i.e. lags, of up to seven days before the actual CMSCC sampling event were added in the dataset to investigate if they were of use in modeling the cell count. Univariable generalized additive models (GAM) were used to screen the data to select potential independent variables. Furthermore, several multivariable GAM were fitted in order to compare the importance of the potential independent variables and to explore how the model performance would be affected by using data from various number of days before the CMSCC sampling event. The result of the model selection showed that the best explanation of CMSCC was provided by the model incorporating all significant variables from the variable screening for the seven preceding days, including the day of the CMSCC sampling event. However, using data from only three days before the CMSCC sampling event is suggested to be sufficient to model CMSCC. Variables combining conductivity quarter data, together with quarter conductivity, are suggested to be important in describing CMSCC. We conclude that CMSCC can be modeled with a high degree of explanation using the information routinely recorded by the milking robot

A Genome Wide Association Study for Longevity in Cattle

Longevity is regarded as the most important functional trait in cattle breeding with high economic value yet low heritability. In order to identify genomic regions associated with longevity, a genome wise association study was performed using data from 4887 Fleckvieh bulls and 33,556 SNPs after quality control. Single SNP regression was used for identification of important SNPs including eigenvectors as a means of correction for population structure. SNPs selected with a false discovery rate threshold of 0.05 and with local false discovery rate identified genomic regions associated with longevity which were subsequently cross checked with the National Center for Biotechnology Information (NCBI) database. This, to identify interesting genes in cattle and their homologue forms in other species. The most notable genes were SYT10 located on chromosome 5, ADAMTS3 on chromosome 6, NTRK2 on chromosome 8 and SNTG1 on chromosome 14 of the cattle genome. Several of the genes found have previously been associated with cattle fertility. Poor fertility is an important culling reason and thereby affects longevity in cattle. Several signals were located in regions sparse with described genes, which suggest that there might be several other non-identified genetic pathways for this important trait