Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background

Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model.

Methods

We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters.

Results

Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed.

Conclusion

The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.     

Genetic analysis of insect bite hypersensitivity (summer eczema) in Icelandic horses

There is a lack of knowledge about the genetic background of eczema due to insect bite hypersensitivity, also called summer eczema, in horses. The condition is known in several horse breeds and countries and it causes reduced welfare of the horse and economic losses to the owner. The aim of this study was to estimate genetic parameters for summer eczema in Swedish-born Icelandic horses. A questionnaire was sent to owners of horses sired by stallions with more than 50 offspring born in Sweden between 1991 and 2001. Variance components of summer eczema classified as healthy, mild, moderate or severe were estimated using the threshold methodology with sire models. In addition, summer eczema was analysed as a binary trait (healthy v. affected). The analyses included 1250 horses sired by 33 stallions. The prevalence of summer eczema was 8%, with a range of 0% to 30% in different paternal half-sib groups. Offspring of dams suffering from eczema had a higher risk of developing eczema. The heritability for severity of summer eczema was estimated at 0.3 (s.d. < 0.2) with a threshold sire model. In contrast to the age of the horse, different geographic areas and gender were significantly associated with severity of the eczema. We conclude that genetic selection could decrease the prevalence of summer eczema among Swedish-born Icelandic horses. The amount and quality of data are, however, crucial for the possibility to introduce a genetic evaluation of summer eczema. The symptoms should be classified in several classes according to severity, and this classification could be made by the horse owner.     

Evidence of independent gene duplications during the evolution of archaeal and eukaryotic family B DNA polymerases

Eukaryotes and archaea both possess multiple genes coding for family B DNA polymerases. In animals and fungi, three family B DNA polymerases, alpha, delta, and epsilon, are responsible for replication of nuclear DNA. We used a PCR-based approach to amplify and sequence phylogenetically conserved regions of these three DNA polymerases from Giardia intestinalis and Trichomonas vaginalis, representatives of early-diverging eukaryotic lineages. Phylogenetic analysis of eukaryotic and archaeal paralogs suggests that the gene duplications that gave rise to the three replicative paralogs occurred before the divergence of the earliest eukaryotic lineages, and that all eukaryotes are likely to possess these paralogs. One eukaryotic paralog, epsilon, consistently branches within archaeal sequences to the exclusion of other eukaryotic paralogs, suggesting that an epsilon-like family B DNA polymerase was ancestral to both archaea and eukaryotes. Because crenarchaeote and euryarchaeote paralogs do not form monophyletic groups in phylogenetic analysis, it is possible that archaeal family B paralogs themselves evolved by a series of gene duplications independent of the gene duplications that gave rise to eukaryotic paralogs.      

Feasibility of a liver transcriptomics approach to assess bovine treatment with the prohormone dehydroepiandrosterone (DHEA)

Background  

Within the European Union the use of growth promoting agents in animal production is prohibited. Illegal use of natural prohormones like dehydroepiandrosterone (DHEA) is hard to prove since prohormones are strongly metabolized in vivo. In the present study, we investigated the feasibility of a novel effect-based approach for monitoring abuse of DHEA. Changes in gene expression profiles were studied in livers of bull calves treated orally (PO) or intramuscularly (IM) with 1000 mg DHEA versus two control groups, using bovine 44K DNA microarrays. In contrast to controlled genomics studies, this work involved bovines purchased at the local market on three different occasions with ages ranging from 6 to 14 months, thereby reflecting the real life inter-animal variability due to differences in age, individual physiology, season and diet.     

Principal component approach in variance component estimation for international sire evaluation

Background

The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.

Methods

This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.

Results

Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.

Conclusions

In terms of estimation''s accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.     

Widespread and ancient distribution of a noncanonical genetic code in diplomonads

Recently, a group of diplomonads has been found to use a genetic code in which TAA and TAG encode glutamine rather than termination. To survey the distribution of this characteristic in diplomonads, we sought to identify TAA and TAG codons at positions where glutamine is expected in genes for alpha-tubulin, elongation factor-1 alpha, and the gamma subunit of eukaryotic translation initiation factor-2. These sequences show that the variant genetic code is utilized by almost all diplomonads, with the genus Giardia alone using the universal genetic code. Comparative phylogenetic analysis reveals that the switch to this genetic code took place very early in the evolution of diplomonads and was likely a single event. Termination signals and downstream untranslated regions were also cloned from three Hexamita genes. In all three of these genes, the predicted TGA termination codon was found at the expected position. Interestingly, the untranslated regions of these genes are high in AT. This is incongruent with the coding regions, which are comparatively GC-rich.      

A Novel Generalized Ridge Regression Method for Quantitative Genetics

As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithm for situations in which the number of parameters largely exceeds the number of observations. The computationally demanding parts of the method depend mainly on the number of observations and not the number of parameters. The algorithm was implemented in the R package bigRR based on the previously developed package hglm. Using such an approach, a heteroscedastic effects model (HEM) was also developed, implemented, and tested. The efficiency for different data sizes were evaluated via simulation. The method was tested for a bacteria-hypersensitive trait in a publicly available Arabidopsis data set including 84 inbred lines and 216,130 SNPs. The computation of all the SNP effects required <10 sec using a single 2.7-GHz core. The advantage in run time makes permutation test feasible for such a whole-genome model, so that a genome-wide significance threshold can be obtained. HEM was found to be more robust than ordinary RR (a.k.a. SNP-best linear unbiased prediction) in terms of QTL mapping, because SNP-specific shrinkage was applied instead of a common shrinkage. The proposed algorithm was also assessed for genomic evaluation and was shown to give better predictions than ordinary RR.