Diurnal variation in genetic parameters for locomotor activity in Drosophila melanogaster assessed under natural thermal conditions

In nature, organisms are exposed to variable and occasionally stressful environmental conditions. Responses to diurnal and seasonal fluctuations, such as temperature and food accessibility, involve adaptive behavioural and physiological changes. While much work has been done on understanding the genetic architecture and evolutionary potential of stress tolerance traits under constant thermal conditions, there has been less focus on the quantitative genetic background in variable environments. In this study, we use the Drosophila Genetic Reference Panel (DGRP) to investigate the locomotor activity, a key behavioural trait, under variable natural thermal conditions during the summer in a temperate environment. Male flies from 100 DGRP lines were exposed to natural thermal and light conditions in Drosophila activity monitors across three experimental days. We found that activity was highly temperature and time dependent and varied between lines both within and between days. Furthermore, we observed variation in genetic and environmental variance components, with low to moderate estimates of the heritability for locomotor activity, consistently peaking in the afternoons. Moreover, we showed that the estimated genetic correlations of locomotor activity between two time points decreased, as the absolute differences in ambient temperature increased. In conclusion, we find that the genetic background for locomotor activity is environment specific, and we conclude that more variable and unpredictable future temperatures will likely have a strong impact on the evolutionary trajectories of behavioural traits in ectotherms.

Expanded utility of the R package, qgg, with applications within genomic medicine

SUMMARY

Here, we present an expanded utility of the R package qgg for genetic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes Bayesian linear regression modeling procedures, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study summary data. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software and data formats.

AVAILABILITY AND IMPLEMENTATION

The methodologies are implemented in the publicly available R software package, qgg, using fast and memory efficient algorithms in C++ and is available on CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available at our GitHub page https://psoerensen.github.io/qgg/.

Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops

Biogas production from anaerobic digestion (AD) of biowastes is restricted by the recalcitrant nature of many substrates, and this may also reduce the fertiliser value of the produced digestate. The degradability of substrates can potentially be enhanced by physico-chemical pre-treatments before AD, and/or the degradation can be increased by a longer digestion time. In this study, we evaluated the effects of electrokinetic (high voltage) and ultrasonication pre-treatments of biowastes in a two-step AD process on nitrogen fertiliser replacement value (NFRV) of digestates obtained from two biogas plants with contrasting hydraulic retention time (HRT) in the primary AD step. The fertiliser value was tested by direct injection to spring barley and surface-banding to winter wheat, and the ammonium N was ¹⁵N-labelled to evaluate ammonia losses. The electrokinetic pre-treatment step significantly (p < 0.05) increased the NH₄⁺-N/total N in the digestates before the second AD step but had an insignificant effect on the fertiliser value in winter wheat and spring barley. Ultrasonication pre-treatment had also no significant effect on the fertiliser value. The two-step AD significantly (p < 0.001) increased ¹⁵N recoveries and mineral fertiliser equivalence of labelled ammonium-N in winter wheat and reduced ammonia losses, with a significant effect (p < 0.001) observed in digestates sourced from a shorter HRT biogas reactor. The fertiliser equivalence of labelled ammonium-N in the digestates was 80-88% after injection, indicating relatively low N immobilisation with all the digestates. NFRV in the crops was mainly explained by the NH₄⁺-N/total N ratio, C/N ratio and dry matter content of the digestates. The findings suggest that electrokinetic and ultrasonication pre-treatments combined with a second AD step have no considerable impact on the fertiliser value of digestates, whereas a second AD step significantly reduced ammonia losses after application by surface-banding in winter wheat.

Nitrogen dynamics and carbon sequestration in soil following application of digestates from one- and two-step anaerobic digestion

Anaerobic digestion (AD) is an important tool for reducing greenhouse gas emissions from agricultural production. A prolonged retention time by adding an extra anaerobic digestion step can be utilized to further degrade the digestates, contributing to increased nitrogen mineralisation and reducing decomposable organic matter. These modifications could influence the potential N fertiliser value of the digestate and soil carbon sequestration after field application. This study investigated the effects of prolonging retention time by implementing an additional anaerobic digestion step on carbon and nitrogen dynamics in the soil and soil carbon sequestration. Two digestates obtained from two biogas plants operating at contrasting hydraulic retention times, with and without an additional digestion step, were applied to a loamy sand soil. N mineralisation dynamics were measured during 80 days and C mineralisation during 212 days. After 80 days of incubation, the net inorganic N release from digestates obtained from a secondary AD step increased by 9-17 % (% of the N input) compared to corresponding digestates obtained from a primary AD step. A kinetic four-pool carbon model was used to fit C mineralisation data to estimate carbon sequestration in the soil. After 212 days of incubation, the net C mineralisation was highest in undigested solid biomass (68 %) and digestates obtained from the primary AD step (59-65 %). The model predicted that 26-54 % of C applied is sequestered in the soil in the long-term. The long-term soil C retention related to the C present before digestion was similar for one- and two-step AD at 12-16 %. We conclude that optimizing the anaerobic digestion configurations by including a secondary AD step could potentially replace more mineral N fertiliser due to an improved N fertiliser value of the resultant digestate without affecting carbon sequestration negatively.

Modelling mercury concentration in Ghanaian soil

Mercury usage in Artisanal Small Scale Gold Mining is a major anthropogenic source of mercury in the environment. In this study, mercury pools and fluxes have been established for Ghana, which has a large ASGM sector, based on estimated losses of mercury to the environment, deposition calculated with GLEMOS, a global long-range transport model for mercury in air, and mercury measured in soils and water in Ghana. A model for mercury in soils and water of Ghana with a resolution of 5 × 5 km² and a monthly or yearly time step has been developed to assess the regional increase in soil and water concentrations that can be attributed to anthropogenic sources and to simulate scenarios into the future. The model has been calibrated to reproduce present-day mercury concentration in the soil (average 0.0193 mg kg^-1) with current deposition calculated with the long-range transport model and past years' deposition based on a scenario for the historic development of the mining activity. This calculation gives an average increase in soil concentrations from anthropogenic sources of 22%. The model gives a fair description of the regional differences in soil concentrations but underestimates concentrations in regions with intense mining activity and overestimates concentrations in regions with less mining when using deposition from the long-range model as input.

Systematic Review of Dairy Processing Sludge and Secondary STRUBIAS Products Used in Agriculture

Worldwide dairy processing plants produce high volumes of dairy processing sludge (DPS), which can be converted into secondary derivatives such as struvite, biochar and ash (collectively termed STRUBIAS). All of these products have high fertilizer equivalent values (FEV), but future certification as phosphorus (P)-fertilizers in the European Union will mean they need to adhere to new technical regulations for fertilizing materials i.e., content limits pertaining to heavy metals (Cd, Cu, Hg, Ni, Pb, and Zn), synthetic organic compounds and pathogens. This systematic review presents the current state of knowledge about these bio-based fertilizers and identifies knowledge gaps. In addition, a review and calculation of greenhouse gas emissions from a range of concept dairy sludge management and production systems for STRUBIAS products [i.e., biochar from pyrolysis and hydrochar from hydrothermal carbonization (HTC)] is presented. Results from the initial review showed that DPS composition depends on product type and treatment processes at a given processing plant, which leads to varied nutrient, heavy metal and carbon contents. These products are all typically high in nutrients and carbon, but low in heavy metals. Further work needs to concentrate on examining their pathogenic microorganism and emerging contaminant contents, in addition to conducting an economic assessment of production and end-user costs related to chemical fertilizer equivalents. With respect to STRUBIAS products, contaminants not present in the raw DPS may need further treatment before being land applied in agriculture e.g., heated producing ashes, hydrochar, or biochar. An examination of these products from an environmental perspective shows that their water quality footprint could be minimized using application rates based on P incorporation of these products into nutrient management planning and application by incorporation into the soil. Results from the concept system showed that elimination of methane emissions was possible, along with a reduction in nitrous oxide. Less carbon (C) is transferred to agricultural fields where DPS is processed into biochar and hydrochar, but due to high recalcitrance, the C in this form is retained much longer in the soil, and therefore STRUBIAS products represent a more stable and long-term option to increase soil C stocks and sequestration.

Multi-Trait Genomic Risk Stratification for Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction

Supplemental Digital Content is available in the text.

Background:

There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment.

Methods:

Using the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals.

Results:

The novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7–7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%–15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6–19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period.

Conclusions:

An IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person’s polygenic risk.

Sulfur from biogas desulfurization: Fate of S during storage in manure and after application to plants

Residues from hydrogen sulfide (H₂S) removal in biogas filters contain sulfur (S) in various forms (sulfate, sulfide, elemental S) that, if properly stored, is potentially valuable as crop fertilizer. We investigated 1) the turnover of the S compounds from filter materials during storage in untreated and digested cattle manure (CM), and 2) the S fertilizer replacement value (SFRV) of the filter materials applied in pure form or mixed manure with and without storage. The S filter materials from four H₂S removal processes (biological and physical-chemical) containing mostly sulfate and/or elemental S were added to untreated CM or digested CM and stored at 10 °C for six months. Afterwards, a pot experiment was established to assess the S availability in an oil-seed rape (Brassica napus) crop. Microbial reduction of sulfate into sulfide took place rapidly after 69 days storage of untreated CM. A lower reduction rate was observed in digested CM mixtures. After six months, 68% and 32% of the initial sulfate content were still present in mixtures containing the S filter materials from biological desulfurization with digested CM and untreated CM, respectively. Sulfate reduction was inhibited for 120 days when digested CM was mixed with S saturated solution from an ash filter, probably due to high pH (≥8.2) and redox potential (>-100 mV) levels. Oppositely, elemental S was immediately and simultaneously both reduced and oxidized. Relatively low losses of total S were observed during the present storage conditions. Despite S turnover, the SFRV of CM and digested CM significantly increased from 15-19% (of total S applied) to 56-90% when S filter materials were added. The storage of S filter materials in digested manure reduced the risk of sulfide production and potential S volatilization. The S filter materials were a valuable source of plant-available S.

Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

Genomic diversity revealed by whole-genome sequencing in three Danish commercial pig breeds

Whole-genome sequencing of 217 animals from three Danish commercial pig breeds (Duroc, Landrace [LL], and Yorkshire [YY]) was performed. Twenty-six million single-nucleotide polymorphisms (SNPs) and 8 million insertions or deletions (indels) were uncovered. Among the SNPs, 493,099 variants were located in coding sequences, and 29,430 were predicted to have a high functional impact such as gain or loss of stop codon. Using the whole-genome sequence dataset as the reference, the imputation accuracy for pigs genotyped with high-density SNP chips was examined. The overall average imputation accuracy for all biallelic variants (SNP and indel) was 0.69, while it was 0.83 for variants with minor allele frequency > 0.1. This study provides whole-genome reference data to impute SNP chip-genotyped animals for further studies to fine map quantitative trait loci as well as improving the prediction accuracy in genomic selection. Signatures of selection were identified both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during breed development or subsequent divergent selection. However, the fixation indices did not indicate a strong divergence among these three breeds. In LL and YY, the integrated haplotype score identified genomic regions under recent selection. These regions contained genes for olfactory receptors and oxidoreductases. Olfactory receptor genes that might have played a major role in the domestication were previously reported to have been under selection in several species including cattle and swine.

Data on initial leaf P concentrations and final dry matter yields of silage maize in response to row-injected cattle slurry

This article displays a dataset obtained in a field trial conducted in 2016 on a sandy loam and a coarse sandy soil, Denmark. Leaf phosphorus (P) and nitrogen (N) concentrations at the five-leaf stage (V5) and final dry matter (DM) yields of silage maize were determined in response to seven treatments with placed slurry below the maize row. Two row-injection methods combined with slurry acidification or addition of a nitrification inhibitor were tested. Furthermore final crop P uptake and P surplus at field level were determined. This dataset can be used to assess the effect of placed slurry with or without slurry acidification and addition of a nitrification inhibitor on silage maize yields and to enhance our knowledge on maize P uptake and P surpluses at field level. In turn this can support the design of appropriate row-injection machinery of slurry. The data supplied in this article is related to the research article entitled "Row-injected cattle slurry can replace mineral P starter fertiliser and reduce P surpluses without compromising final yields of silage maize" [1], where results from 2017 and 2018 are presented and discussed. The trials in 2016, 2017 and 2018 were conducted on the same study sites. The experimental design in 2017 and 2018 was a full-factorial design and did also include reference treatments with evenly injected slurry, whereas these reference treatments were not included in the present article.

qgg: an R package for large-scale quantitative genetic analyses

Summary

Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes.

Availability and implementation

The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic Signatures of Drug Response Variability in Drosophila melanogaster

Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases.

Understanding the potential bias of variance components estimators when using genomic models

BACKGROUND

Genomic models that link phenotypes to dense genotype information are increasingly being used for infering variance parameters in genetics studies. The variance parameters of these models can be inferred using restricted maximum likelihood, which produces consistent, asymptotically normal estimates of variance components under the true model. These properties are not guaranteed to hold when the covariance structure of the data specified by the genomic model differs substantially from the covariance structure specified by the true model, and in this case, the likelihood of the model is said to be misspecified. If the covariance structure specified by the genomic model provides a poor description of that specified by the true model, the likelihood misspecification may lead to incorrect inferences.

RESULTS

This work provides a theoretical analysis of the genomic models based on splitting the misspecified likelihood equations into components, which isolate those that contribute to incorrect inferences, providing an informative measure, defined as [Formula: see text], to compare the covariance structure of the data specified by the genomic and the true models. This comparison of the covariance structures allows us to determine whether or not bias in the variance components estimates is expected to occur.

CONCLUSIONS

The theory presented can be used to provide an explanation for the success of a number of recently reported approaches that are suggested to remove sources of bias of heritability estimates. Furthermore, however complex is the quantification of this bias, we can determine that, in genomic models that consider a single genomic component to estimate heritability (assuming SNP effects are all i.i.d.), the bias of the estimator tends to be downward, when it exists.

Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait

Most organisms experience variable and sometimes suboptimal environments in their lifetime. While stressful environmental conditions are normally viewed as a strong selective force, they can also impact directly on the genetic basis of traits such as through environment-dependent gene action. Here, we used the Drosophila melanogaster Genetic Reference Panel to investigate the impact of developmental temperature on variance components and evolutionary potential of cold tolerance. We reared 166 lines at five temperatures and assessed cold tolerance of adult male flies from each line and environment. We show (1) that the expression of genetic variation for cold tolerance is highly dependent on developmental temperature, (2) that the genetic correlation of cold tolerance between environments decreases as developmental temperatures become more distinct, (3) that the correlation between cold tolerance at individual developmental temperatures and plasticity for cold tolerance differs across developmental temperatures, and even switches sign across the thermal developmental gradient, and (4) that evolvability decrease with increasing developmental temperatures. Our results show that the quantitative genetic basis of low temperature tolerance is environment specific. This conclusion is important for the understanding of evolution in variable thermal environments and for designing experiments aimed at pinpointing candidate genes and performing functional analyses of thermal resistance.

Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds

Background

A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of “Gene Ontology” (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability.

Results

Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020).

Conclusions

Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4004-z) contains supplementary material, which is available to authorized users.

Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior

Human psychiatric disorders such as schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype-by-social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments that turned out to be enriched for genome-wide associated variants. A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice. Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.

Novel approaches and perspectives in allergen immunotherapy

In this review, we report on relevant current topics in allergen immunotherapy (AIT) which were broadly discussed during the first Aarhus Immunotherapy Symposium (Aarhus, Denmark) in December 2015 by leading clinicians, scientists and industry representatives in the field. The aim of this symposium was to highlight AIT-related aspects of public health, clinical efficacy evaluation, mechanisms, development of new biomarkers and an overview of novel therapeutic approaches. Allergy is a public health issue of high socioeconomic relevance, and development of evidence-based action plans to address allergy as a public health issue ought to be on national and regional agendas. The underlying mechanisms are in the focus of current research that lays the ground for innovative therapies. Standardization and harmonization of clinical endpoints in AIT trials as well as current knowledge about potential biomarkers have substantiated proof of effectiveness of this disease-modifying therapeutic option. Novel treatments such as peptide immunotherapy, intralymphatic immunotherapy and use of recombinant allergens herald a new age in which AIT may address treatment of allergy as a public health issue by reaching a large fraction of patients.

Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster

The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel.

Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection

BACKGROUND

A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model.

RESULTS

We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05).

CONCLUSIONS

GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.

Comparison of growth performance and immune parameters of three commercial chicken lines used in organic production

Owing to the higher demands for avoiding medication and antibiotics, health status of the production animals plays an important role in the poultry industry, especially in organic poultry systems. Immunity plays a major role in keeping the host free from disease, and it is evident that the host's genetic make-up influences immunity and disease resistance/susceptibility in chickens. Previously, breeding strategies aimed at selection for resistance against specific diseases with the risk of creating less disease resistance against other pathogens. Changing breeding strategies towards selection of chickens with a more general and broad disease resistance or robustness may therefore improve the overall health status, animal welfare, and food security in the poultry production. The aim of this study was therefore to compare the immunocompetence of the presumed "robust" Hellevad chickens with two chicken lines widely used in organic production, Bovans Brown (Bovans) and Hisex White (Hisex). The chickens were subjected to a routine vaccination program comprising one parasite and four viral vaccines. The current study indicates that considerable differences in immunocompetence may exist between commercial layer lines used in organic production. The Hellevad chickens were found to have higher body weight at the end of the experiment (17 weeks of age) than the other two lines. Furthermore, Hellevad and Hisex chickens were found to have higher levels of humoral innate immunity with regard to sample to positive ratio of natural antibodies in serum and concentration of mannose-binding lectin in serum as compared to Bovans. Moreover, indications of an inflammatory response were observed in the Bovans at week 5, corresponding to 1 week after vaccination with live infectious bursal disease virus. With regard to adaptive immune parameters such as IgY concentration in blood and infectious bursal disease virus (IBDV)-specific antibody titres, the Hellevad and Hisex chickens had lower levels than the Bovans. How the differences observed in growth and immune parameters in the three chicken lines influence the immune protection against infection needs to be studied further.

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.

Barriers toward organ donation in a Danish University Hospital

BACKGROUND

In Denmark, organ donation-rates are below the average in the western countries. We investigated the donor potential and identified barriers toward organ donation in a Danish university hospital.

METHODS

All patients who died in Aalborg University Hospital in 2012 were retrospectively identified. Patients with a CT- or MRI-proven deadly brain-lesion were eligible for inclusion.

RESULTS

Eighty-five patients with deadly brain-lesions were included, and of these 47 patients died in the intensive care unit (ICU). Older age and diagnosis of brain-hemorrhage and infarction were associated with admission to general ward (GW). In 62.4% of the patients the potential of becoming a donor was not identified. No donations occurred from patients dying from intracerebral hemorrhage or brain-infarction although they represented 44.7% of the potential donors.

DISCUSSION

This study reveals a huge, unrecognized donation potential at our hospital. About 30% was lost because they were never admitted to the ICU. After primary admission to the ICU, 15.3% of the potential donors were lost because they were transferred to the GW. In patients who died in the ICU 17.6% of the patients were not evaluated as potential donors. The relatives refused donation in 17.6% of cases.

CONCLUSION

It would be possible to raise the donation rate considerably if patients with donation potential are intubated and admitted to the ICU. When active treatment is considered withdrawn, possibility of organ donation should be evaluated, and the next of kin be approached by experienced staff.

Risk assessment of replacing conventional P fertilizers with biomass ash: Residual effects on plant yield, nutrition, cadmium accumulation and mycorrhizal status

Reutilizing biomass ashes in agriculture can substitute inputs of P from finite primary sources. However, recycling of ashes is disputed due to their content of toxic substances such as heavy metals. This study evaluates the potential risk of replacing easily soluble inorganic P fertilizer with P in biomass ashes in a barley crop grown on soil with adequate P status. Two contrasting doses of three different types of ashes were applied to an agricultural field with spring barley and compared to similar doses of triple-superphosphate fertilizer. In the second growing season after biomass ash application, grain, straw and root dry matter yield, and P and Cd uptake were determined. Resin-extractable P was measured in soil and the symbiotic arbuscular mycorrhizal fungal activity, colonization, and community composition were assessed. Crop yield was not affected by ash application, while P-uptake and mycorrhizal status were slightly enhanced with high ash applications. Changes to the mycorrhizal community composition were evident with high ash doses. Cadmium uptake in aboveground plant tissue was unaffected by ash treatments, but increased in roots with increasing doses. Consequently, we conclude that fertilization with biomass ashes can replace conventional fertilizers without risk to barley crops in the short term.

Genome-Wide Transcriptional and Post-transcriptional Regulation of Innate Immune and Defense Responses of Bovine Mammary Gland to Staphylococcus aureus

Staphylococcus aureus (S. aureus) is problematic for lactating mammals and public health. Understanding of mechanisms by which the hosts respond to severe invasion of S. aureus remains elusive. In this study, the genome-wide expression of mRNAs and miRNAs in bovine mammary gland cells were interrogated at 24 h after intra-mammary infection (IMI) with high or low concentrations of S. aureus. Compared to the negative control quarters, 194 highly-confident responsive genes were identified in the quarters with high concentration (10⁹ cfu/mL) of S. aureus, which were predominantly implicated in pathways and biological processes pertaining to innate immune system, such as cytokine-cytokine receptor interaction and inflammatory response. In contrast, only 21 highly-confident genes were significantly differentially expressed in face of low concentration (10⁶ cfu/mL) of S. aureus, which slightly perturbed the cell signaling and invoked corresponding responses like vasoconstriction, indicating limited perturbations and immunological evading. Additionally, the significant up-regulations of bta-mir-223 and bta-mir-21-3p were observed in the quarters infected by high concentration of S. aureus. Network analysis suggested that the two miRNAs' pivotal roles in defending hosts against bacterial infection probably through inhibiting CXCL14 and KIT. The significant down-regulation of CXCL14 was also observed in bovine mammary epithelial cells at 24 h post-infection of S. aureus (10⁸ cfu/mL) in vitro. Integrated analysis with QTL database further suggested 28 genes (e.g., CXCL14, KIT, and SLC4A11) as candidates of bovine mastitis. This study first systematically revealed transcriptional and post-transcriptional responses of bovine mammary gland cells to invading S. aureus in a dosage-dependent pattern, and highlighted a complicated responsive mechanism in a network of miRNA-gene-pathway interplay.

High plant availability of phosphorus and low availability of cadmium in four biomass combustion ashes

For biomass combustion to become a sustainable energy production system, it is crucial to minimise landfill of biomass ashes, to recycle the nutrients and to minimise the undesirable impact of hazardous substances in the ash. In order to test the plant availability of phosphorus (P) and cadmium (Cd) in four biomass ashes, we conducted two pot experiments on a P-depleted soil and one mini-plot field experiment on a soil with adequate P status. Test plants were spring barley and Italian ryegrass. Ash applications were compared to triple superphosphate (TSP) and a control without P application. Both TSP and ash significantly increased crop yields and P uptake on the P-depleted soil. In contrast, on the adequate-P soil, the barley yield showed little response to soil amendment, even at 300-500kgPha(-1) application, although the barley took up more P at higher applications. The apparent P use efficiency of the additive was 20% in ryegrass - much higher than that of barley for which P use efficiencies varied on the two soils. Generally, crop Cd concentrations were little affected by the increasing and high applications of ash, except for relatively high Cd concentrations in barley after applying 25Mgha(-1) straw ash. Contrarily, even modest increases in the TSP application markedly increased Cd uptake in plants. This might be explained by the low Cd solubility in the ash or by the reduced Cd availability due to the liming effect of ash. High concentrations of resin-extractable P (available P) in the ash-amended soil after harvest indicate that the ash may also contribute to P availability for the following crops. In conclusion, the biomass ashes in this study had P availability similar to the TSP fertiliser and did not contaminate the crop with Cd during the first year.

The Impact of Intramammary Escherichia coli Challenge on Liver and Mammary Transcriptome and Cross-Talk in Dairy Cows during Early Lactation Using RNAseq

Our objective was to identify the biological response and the cross-talk between liver and mammary tissue after intramammary infection (IMI) with Escherichia coli (E. coli) using RNAseq technology. Sixteen cows were inoculated with live E. coli into one mammary quarter at ~4–6 weeks in lactation. For all cows, biopsies were performed at -144, 12 and 24 h relative to IMI in liver and at 24 h post-IMI in infected and non-infected (control) mammary quarters. For a subset of cows (n = 6), RNA was extracted from both liver and mammary tissue and sequenced using a 100 bp paired-end approach. Ingenuity Pathway Analysis and the Dynamic Impact Approach analysis of differentially expressed genes (overall effect False Discovery Rate≤0.05) indicated that IMI induced an overall activation of inflammation at 12 h post-IMI and a strong inhibition of metabolism, especially related to lipid, glucose, and xenobiotics at 24 h post-IMI in liver. The data indicated in mammary tissue an overall induction of inflammatory response with little effect on metabolism at 24 h post-IMI. We identified a large number of up-stream regulators potentially involved in the response to IMI in both tissues but a relatively small core network of transcription factors controlling the response to IMI for liver whereas a large network in mammary tissue. Transcriptomic results in liver and mammary tissue were supported by changes in inflammatory and metabolic mediators in blood and milk. The analysis of potential cross-talk between the two tissues during IMI uncovered a large communication from the mammary tissue to the liver to coordinate the inflammatory response but a relatively small communication from the liver to the mammary tissue. Our results indicate a strong induction of the inflammatory response in mammary tissue and impairment of liver metabolism 24h post-IMI partly driven by the signaling from infected mammary tissue.

Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs

Background

In animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features.

Results

We analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits.

Conclusions

The GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0322-9) contains supplementary material, which is available to authorized users.

Decomposing genomic variance using information from GWA, GWE and eQTL analysis

A commonly used procedure in genome-wide association (GWA), genome-wide expression (GWE) and expression quantitative trait locus (eQTL) analyses is based on a bottom-up experimental approach that attempts to individually associate molecular variants with complex traits. Top-down modeling of the entire set of genomic data and partitioning of the overall variance into subcomponents may provide further insight into the genetic basis of complex traits. To test this approach, we performed a whole-genome variance components analysis and partitioned the genomic variance using information from GWA, GWE and eQTL analyses of growth-related traits in a mouse F2 population. We characterized the mouse trait genetic architecture by ordering single nucleotide polymorphisms (SNPs) based on their P-values and studying the areas under the curve (AUCs). The observed traits were found to have a genomic variance profile that differed significantly from that expected of a trait under an infinitesimal model. This situation was particularly true for both body weight and body fat, for which the AUCs were much higher compared with that of glucose. In addition, SNPs with a high degree of trait-specific regulatory potential (SNPs associated with subset of transcripts that significantly associated with a specific trait) explained a larger proportion of the genomic variance than did SNPs with high overall regulatory potential (SNPs associated with transcripts using traditional eQTL analysis). We introduced AUC measures of genomic variance profiles that can be used to quantify relative importance of SNPs as well as degree of deviation of a trait's inheritance from an infinitesimal model. The shape of the curve aids global understanding of traits: The steeper the left-hand side of the curve, the fewer the number of SNPs controlling most of the phenotypic variance.

Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle

Background

We have used a linear mixed model (LMM) approach to examine the joint contribution of genetic markers associated with a biological pathway. However, with these markers being scattered throughout the genome, we are faced with the challenge of modelling the contribution from several, sometimes even all, chromosomes at once. Due to linkage disequilibrium (LD), all markers may be assumed to account for some genomic variance; but the question is whether random sets of markers account for the same genomic variance as markers associated with a biological pathway?

Results

We applied the LMM approach to identify biological pathways associated with udder health and milk production traits in dairy cattle. A random gene sampling procedure was applied to assess the biological pathways in a dataset that has an inherently complex genetic correlation pattern due to the population structure of dairy cattle, and to linkage disequilibrium within the bovine genome and within the genes associated to the biological pathway.

Conclusions

Several biological pathways that were significantly associated with health and production traits were identified in dairy cattle; i.e. the markers linked to these pathways explained more of the genomic variance and provided a better model fit than 95 % of the randomly sampled gene groups. Our results show that immune related pathways are associated with production traits, and that pathways that include a causal marker for production traits are identified with our procedure.

We are confident that the LMM approach provides a general framework to exploit and integrate prior biological information and could potentially lead to improved understanding of the genetic architecture of complex traits and diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0132-6) contains supplementary material, which is available to authorized users.

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster

Individuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance, and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific, and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90, setting the stage for future genetic dissection of this phenomenon.

Effects of high hydrostatic pressure on genomic expression profiling of porcine parthenogenetic activated and cloned embryos

Handmade cloning (HMC) has been used to generate transgenic pigs for biomedical research. Recently, we found that parthenogenetic activation (PA) of porcine oocytes and improved HMC efficiency could be achieved by treatment with sublethal high hydrostatic pressure (HHP). However, the molecular mechanism underlying the effects of HHP treatment on embryonic development is poorly understood and so was investigated in the present study. Thus, in the present study, we undertook genome-wide gene expression analysis in HHP-treated and untreated oocytes, as well as in 4-cell and blastocyst stage embryos derived by PA or HMC. Hierarchical clustering depicted stage-specific genomic expression profiling. At the 4-cell and blastocyst stages, 103 and 163 transcripts were differentially expressed between the HMC and PA embryos, respectively (P<0.05). These transcripts are predominantly involved in regulating cellular differentiation, gene expression and cell-to-cell signalling. We found that 44 transcripts were altered by HHP treatment, with most exhibiting lower expression in HHP-treated oocytes. Genes involved in embryonic development were prominent among the transcripts affected by HHP. Two of these genes (INHBB and ME3) were further validated by quantitative reverse transcription-polymerase chain reaction. We also observed that HHP treatment activated expression of the imprinting gene DLX5 in 4-cell PA embryos. In conclusion, our genomic expression profiling data suggest that HHP alters the RNA constitution in porcine oocytes and affects the expression of imprinting genes during embryonic development.

Genome-wide association and biological pathway analysis for milk-fat composition in Danish Holstein and Danish Jersey cattle

Background

The milk fat profile of the Danish Holstein (DH) and Danish Jersey (DJ) show clear differences. Identification of the genomic regions, genes and biological pathways underlying the milk fat biosynthesis will improve the understanding of the biology underlying bovine milk fat production and may provide new possibilities to change the milk fat composition by selective breeding. In this study a genome wide association scan (GWAS) in the DH and DJ was performed for a detailed milk fatty acid (FA) profile using the HD bovine SNP array and subsequently a biological pathway analysis based on the SNP data was performed.

Results

The GWAS identified in total 1,233 SNPs (FDR < 0.10) spread over 18 chromosomes for nine different FA traits for the DH breed and 1,122 SNPs (FDR < 0.10) spread over 26 chromosomes for 13 different FA traits were detected for the DJ breed. Of these significant SNPs, 108 SNP markers were significant in both DH and DJ (C14-index, BTA26; C16, BTA14; fat percentage (FP), BTA14). This was supported by an enrichment test. The QTL on BTA14 and BTA26 represented the known candidate genes DGAT and SCD. In addition we suggest ACSS3 to be a good candidate gene for the QTL on BTA5 for C10:0 and C15:0. In addition, genetic correlations between the FA traits within breed showed large similarity across breeds. Furthermore, the biological pathway analysis revealed that fat digestion and absorption (KEGG04975) plays a role for the traits FP, C14:1, C16 index and C16:1.

Conclusion

There was a clear similarity between the underlying genetics of FA in the milk between DH and DJ. This was supported by the fact that there was substantial overlap between SNPs for FP, C14 index, C14:1, C16 index and C16:1. In addition genetic correlations between FA showed a similar pattern across DH and DJ. Furthermore the biological pathway analysis suggested that fat digestion and absorption KEGG04975 is important for the traits FP, C14:1, C16 index and C16:1.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1112) contains supplementary material, which is available to authorized users.

Changes in various metabolic parameters in blood and milk during experimental Escherichia coli mastitis for primiparous Holstein dairy cows during early lactation

Background

The objective of this study was to characterize the changes in various metabolic parameters in blood and milk during IMI challenge with Escherichia coli (E. coli) for dairy cows during early lactation. Thirty, healthy primiparous Holstein cows were infused (h = 0) with ~20-40 cfu of live E. coli into one front mammary quarter at ~4-6 wk in lactation. Daily feed intake and milk yield were recorded. At –12, 0, 3, 6, 12, 18, 24, 36, 48, 60, 72, 96, 108, 120, 132, 144, 156, 168, 180 and 192 h relative to challenge rectal temperatures were recorded and quarter foremilk was collected for analysis of shedding of E. coli. Composite milk samples were collected at -180, -132, -84, -36, -12, 12, 24, 36, 48, 60, 72, 84, 96, 132 and 180 h relative to challenge (h = 0) and analyzed for lactate dehydrogenase (LDH), somatic cell count, fat, protein, lactose, citrate, beta-hydroxybutyrate (BHBA), free glucose (fglu), and glucose-6-phosphate (G6P). Blood was collected at -12, 0, 3, 6, 12, 18, 24, 36, 60, 72, 84, 132 and 180 h relative to challenge and analyzed for plasma non-esterified fatty acids (NEFA), BHBA and glucose concentration. A generalized linear mixed model was used to determine the effect of IMI challenge on metabolic responses of cows during early lactation.

Results

By 12 h, E. coli was recovered from challenged quarters and shedding continued through 72 h. Rectal temperature peaked by 12 h post-challenge and returned to pre-challenge values by 36 h post-IMI challenge. Daily feed intake and milk yield decreased (P <0.05) by 1 and 2 d, respectively, after mastitis challenge. Plasma BHBA decreased (12 h; P <0.05) from 0.96 ± 1.1 at 0 h to 0.57 ± 0.64 mmol/L by 18 h whereas concentration of plasma NEFA (18 h) and glucose (24 h) were significantly greater, 11 and 27%, respectively, after challenge. In milk, fglu, lactose, citrate, fat and protein yield were lower whereas yield of BHBA and G6P were higher after challenge when compared to pre-challenge values.

Conclusions

Changes in metabolites in blood and milk were most likely associated with drops in feed intake and milk yield. However, the early rise in plasma NEFA may also signify enhanced adipose tissue lipolysis. Lower concentrations of plasma BHBA may be attributed to an increase transfer into milk after IMI. Decreases in both milk lactose yield and % after challenge may be partly attributed to reduced conversion of fglu to lactose. Rises in G6P yield and concentration in milk after challenge (24 h) may signify increased conversion of fglu to G6P. Results identify changes in various metabolic parameters in blood and milk after IMI challenge with E. coli in dairy cows that may partly explain the partitioning of nutrients and changes in milk components after IMI for cows during early lactation.

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records

BACKGROUND

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.

RESULTS

We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.

CONCLUSION

We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Flies who cannot take the heat: genome-wide gene expression analysis of temperature-sensitive lethality in an inbred line of Drosophila melanogaster

Fitness decreases associated with inbreeding depression often become more pronounced in a stressful environment. The functional genomic causes of these inbreeding-by-environment (I × E) interactions, and of inbreeding depression in general, are poorly known. To further our understanding of I × E interactions, we performed a genome-wide gene expression study of a single inbred line that suffers from temperature-sensitive lethality. We confirmed that increased differential expression between the thermosensitive line and the control line occurs at the restrictive temperature. This demonstrates that I × E interactions in survival are reflected in similar I × E interactions at the gene expression level. To make an impression of the cellular response associated with the lethal effect, we analysed all functional annotation terms that were overrepresented among the differentially expressed genes. Some sets of differentially expressed genes function in the general stress response, and these are more likely to also be differentially expressed in other studies of inbreeding, inbreeding depression, immunity and heat stress. Other sets of differentially expressed genes are shared with studies of gene expression in inbred lines, but not studies of the response to extrinsic stress, and represent a general transcriptomic signature of inbreeding. Finally, some sets of genes have an annotation that is not reported in other studies. These we consider to be candidates for the genes harbouring the mutations responsible for the thermosensitive phenotype, as these mutations are expected to be unique to this line. These genes may also serve as candidate QTL in studies of thermal tolerance and heat resistance.

Comparison of genome-wide association methods in analyses of admixed populations with complex familial relationships

Population structure is known to cause false-positive detection in association studies. We compared the power, precision, and type-I error rates of various association models in analyses of a simulated dataset with structure at the population (admixture from two populations; P) and family (K) levels. We also compared type-I error rates among models in analyses of publicly available human and dog datasets. The models corrected for none, one, or both structure levels. Correction for K was performed with linear mixed models incorporating familial relationships estimated from pedigrees or genetic markers. Linear models that ignored K were also tested. Correction for P was performed using principal component or structured association analysis. In analyses of simulated and real data, linear mixed models that corrected for K were able to control for type-I error, regardless of whether they also corrected for P. In contrast, correction for P alone in linear models was insufficient. The power and precision of linear mixed models with and without correction for P were similar. Furthermore, power, precision, and type-I error rate were comparable in linear mixed models incorporating pedigree and genomic relationships. In summary, in association studies using samples with both P and K, ancestries estimated using principal components or structured assignment were not sufficient to correct type-I errors. In such cases type-I errors may be controlled by use of linear mixed models with relationships derived from either pedigree or from genetic markers.

Genome-wide and local pattern of linkage disequilibrium and persistence of phase for 3 Danish pig breeds

BACKGROUND

The extent of linkage disequilibrium (LD) is of critical importance for genomic selection and marker assisted selection. The primary purpose of this study is to examine patterns of LD in three Danish pig breeds (Duroc, Landrace and Yorkshire); we also examine patterns of persistence of phase between the breeds. We quantify local LD by fitting a model relating LD to physical distance between markers in sliding windows, and use this to visualize how LD varies according to physical position. We use a similar method to examine local persistence of phase.

RESULTS

Average LD decay over distance for Duroc was significantly different from Landrace and Yorkshire, that showed similar patterns. Persistence of phase between Landrace and Yorkshire was much higher than between these breeds and Duroc. Local r2 over the chromosomes showed more variation between breeds than average r2 decay across whole genome. Also local persistence of phase was higher between Landrace and Yorkshire than between these breeds and Duroc.

CONCLUSIONS

The results concerning genome-wide LD indicated that Duroc had "old inbreeding", and confirmed the mixture history of Landrace and Yorkshire, which is also implied by the higher level of persistence of phase between Landrace and Yorkshire. The method to estimate and visualize local pattern of LD and persistence of phase provides insight into how these quantities vary along chromosomes and between breeds.

The long-term effects of a life-prolonging heat treatment on the Drosophila melanogaster transcriptome suggest that heat shock proteins extend lifespan

Heat-induced hormesis, i.e. the beneficial effect of mild heat-induced stress, increases the average lifespan of many organisms. This effect, which depends on the heat shock factor, decreases the log mortality rate weeks after the stress has ceased. To identify candidate genes that mediate this lifespan-prolonging effect late in life, we treated flies with mild heat stress (34 °C for 2 h) 3 times early in life and compared the transcriptomic response in these flies versus non-heat-treated controls 10-51 days after the last heat treatment. We found significant transcriptomic changes in the heat-treated flies. Several hsp70 probe sets were up-regulated 1.7-2-fold in the mildly stressed flies weeks after the last heat treatment (P<0.01). This result was unexpected as the major Drosophila heat shock protein, Hsp70, is reported to return to normal levels of expression shortly after heat stress. We conclude that the heat shock response, and Hsp70 in particular, may be central to the heat-induced increase in the average lifespan in flies that are exposed to mild heat stress early in life.

Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining

Reproduction is of significant economic importance in dairy cattle. Improved understanding of mechanisms that control estrous behavior and other reproduction traits could help in developing strategies to improve and/or monitor these traits. The objective of this study was to predict and rank genes and processes in brain areas and pituitary involved in reproductive traits in cattle using information derived from three different data sources: gene expression, protein-protein interactions, and literature. We identified 59, 89, 53, 23, and 71 genes in bovine amygdala, dorsal hypothalamus, hippocampus, pituitary, and ventral hypothalamus, respectively, potentially involved in processes underlying estrus and estrous behavior. Functional annotation of the candidate genes points to a number of tissue-specific processes of which the "neurotransmitter/ion channel/synapse" process in the amygdala, "steroid hormone receptor activity/ion binding" in the pituitary, "extracellular region" in the ventral hypothalamus, and "positive regulation of transcription/metabolic process" in the dorsal hypothalamus are most prominent. The regulation of the functional processes in the various tissues operate at different biological levels, including transcriptional, posttranscriptional, extracellular, and intercellular signaling levels.