Search

SummaryAccurate gene prediction is essential for successful metagenome analysis. We present KOunt, a Snake-make pipeline, that precisely quantifies KEGG orthologue abundance.Availability and implementationKOunt is available on GitHub:https://github.com/WatsonLab/KOunt. The KOunt reference database is available on figshare:https://figshare.com/s/72fe3ed7dfbbdcd8b1e5. Test data are available athttps://figshare.com/ndownloader/files/39545968and version 1.1.0 of KOunt athttps://figshare.com/ndown-loader/files/39547273.Contactjennifer.mattock@roslin.ed.ac.ukSupplementary informationSupplementary data are available atBioinformaticsonline.

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

CuInS2 nanocrystals with the wurtzite structure show promise for applications requiring efficient energy transport due to their anisotropic crystal structure. We investigate the source of photoluminescence in the near-infrared spectral region recently observed from these nanocrystals. Spectroscopic studies of both wurtzite CuInS2 itself and samples alloyed with Cd or Zn allow the assignment of this emission to a radiative point defect within the nanocrystal structure. Further, by varying the organic passivation layer on the material, we are able to determine that the atomic species responsible for nonradiative decay paths on the nanocrystal surface are Cu- or S-based. Density functional theory calculations of defect states within the material allow identification of the likely radiative species. Understanding both the electronic structure and optical properties of wurtzite CuInS2 nanocrystals is necessary for their efficient integration into potential biological, photovoltaic, and photocatalytic applications.

OBJECTIVE To determine the frequency of malignancy and survival rates of dogs that underwent splenectomy for incidentally detected nonruptured splenic masses or nodules. DESIGN Retrospective case series. ANIMALS 105 client-owned dogs. PROCEDURES Medical records of dogs that underwent splenectomy at a veterinary teaching hospital between 2009 and 2013 were examined to identify patients with incidentally detected nonruptured splenic masses or nodules without associated hemoperitoneum. Only dogs with histologically confirmed diagnoses were included. Information regarding signalment, preoperative diagnostic tests, perioperative blood product transfusions, splenic mass diameter, histologic findings, adjunctive treatments, and survival time was collected and analyzed. RESULTS 74 of 105 (70.5%) patients had benign splenic lesions and 31 (29.5%) had malignant neoplasia, most commonly hemangiosarcoma (18/31 [58%]). The hazard of death decreased as preoperative PCV increased; histopathologic diagnosis of malignant neoplasia was significantly associated with an increased hazard of death. Median life expectancy of dogs with benign and malignant lesions was 436 and 110 days, respectively; 41 of 74 patients with benign lesions and 3 of 31 patients with malignant neoplasia were still alive at study conclusion. Median life expectancy of dogs with hemangiosarcoma was 132 days; only 7 of these 18 dogs received any adjunctive chemotherapeutic treatments. CONCLUSIONS AND CLINICAL RELEVANCE Incidentally found, nonruptured splenic masses or nodules without associated hemoperitoneum were most commonly benign. Results suggested that life expectancy for these dogs with incidentally detected benign or malignant splenic lesions that received prompt intervention was better than has previously been reported for other studied populations.

After the publication of this work [1], we noticed thatFigs. 1 and 2 were accidentally interchanged. The correctversion order of Figs. 1 and 2 are provided here. Theoriginal article was corrected.

Background Genome editing (GE) is a method that enables specific nucleotides in the genome of an individual to be changed. To date, use of GE in livestock has focussed on simple traits that are controlled by a few quantitative trait nucleotides (QTN) with large effects. The aim of this study was to evaluate the potential of GE to improve quantitative traits that are controlled by many QTN, referred to here as promotion of alleles by genome editing (PAGE). Methods Multiple scenarios were simulated to test alternative PAGE strategies for a quantitative trait. They differed in (i) the number of edits per sire (0 to 100), (ii) the number of edits per generation (0 to 500), and (iii) the extent of use of PAGE (i.e. editing all sires or only a proportion of them). The base line scenario involved selecting individuals on true breeding values (i.e., genomic selection only (GS only)-genomic selection with perfect accuracy) for several generations. Alternative scenarios complemented this base line scenario with PAGE (GS + PAGE). The effect of different PAGE strategies was quantified by comparing response to selection, changes in allele frequencies, the number of distinct QTN edited, the sum of absolute effects of the edited QTN per generation, and inbreeding. Results Response to selection after 20 generations was between 1.08 and 4.12 times higher with GS + PAGE than with GS only. Increases in response to selection were larger with more edits per sire and more sires edited. When the total resources for PAGE were limited, editing a few sires for many QTN resulted in greater response to selection and inbreeding compared to editing many sires for a few QTN. Between the scenarios GS only and GS + PAGE, there was little difference in the average change in QTN allele frequencies, but there was a major difference for the QTN with the largest effects. The sum of the effects of the edited QTN decreased across generations. Conclusions This study showed that PAGE has great potential for application in livestock breeding programs, but inbreeding needs to be managed. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0135-3) contains supplementary material, which is available to authorized users.

Genomic selection (GS) is the use of statistical methods to estimate the genetic merit of a genotyped animal based on prediction equations derived from large ancestral populations with both phenotypes and genotypes. It has revolutionized the dairy cattle breeding industry and has been implemented with varying degrees of success in other animal breeding programs, including swine, poultry, and beef cattle. The findings of empirical field studies applying GS to the breeding sectors of these main animal protein industries are reviewed. Several translational considerations must be addressed before implementing GS in genetic improvement programs. These include determining and obtaining economically relevant phenotypes and determining the optimal size of the training population, cost-effective genotyping strategies, the practicality of field implementation, and the relative costs versus the benefits of the realized rate of genetic gain. GS may additionally change the optimal breeding scheme design, and studies that address this consideration are also reviewed briefly.

Genotype imputation is a cost-effective way to increase the power of genomic selection or genome-wide association studies. While several genotype imputation algorithms are available, this chapter focuses on a heuristic algorithm, as implemented in the AlphaImpute software. This algorithm combines long-range phasing, haplotype library imputation, and segregation analysis and it is specifically designed to work with pedigreed populations.The chapter is organized in different sections. First the challenges related to genotype imputation in pedigreed populations are described, along with the specifics of the imputation algorithm used in AlphaImpute. In the second section, factors affecting the accuracy of genotype imputation using this algorithm are discussed. The different parameters that control AlphaImpute are detailed and examples of how to apply AlphaImpute are given.

Genotype imputation is a cost-effective way to increase the power of genomic selection or genome-wide association studies. While several genotype imputation algorithms are available, this chapter focuses on a heuristic algorithm, as implemented in the AlphaImpute software. This algorithm combines long-range phasing, haplotype library imputation, and segregation analysis and it is specifically designed to work with pedigreed populations.The chapter is organized in different sections. First the challenges related to genotype imputation in pedigreed populations are described, along with the specifics of the imputation algorithm used in AlphaImpute. In the second section, factors affecting the accuracy of genotype imputation using this algorithm are discussed. The different parameters that control AlphaImpute are detailed and examples of how to apply AlphaImpute are given.

The availability of genetic markers in many species has enabled the analysis of marker-trait associations ranging from small genomic regions to genome-wide scale. An appropriate set of markers must be identified to meet the objectives of any research, using a custom discovery and selection approach or by using a commercial product. The key considerations in selecting markers are the quantity and the distribution across the genome. Though decisions about how many markers to use are often pragmatic, influenced by costs and available technology, an evaluation of the marker coverage is important in understanding how to design an effective genomic research study with reasonable expectations about the power to obtain desired results. An important parameter to evaluate coverage is linkage disequilibrium, which can be used to determine the appropriate number of markers for a particular analysis and is related to the proportion of variance that can be explained by a given marker, or power. Finally, the type of analysis used to identify marker-trait associations may depend on marker coverage as the optimal approach, from a statistical or computational standpoint, may differ with changes in marker number and distribution.

The availability of genetic markers in many species has enabled the analysis of marker-trait associations ranging from small genomic regions to genome-wide scale. An appropriate set of markers must be identified to meet the objectives of any research, using a custom discovery and selection approach or by using a commercial product. The key considerations in selecting markers are the quantity and the distribution across the genome. Though decisions about how many markers to use are often pragmatic, influenced by costs and available technology, an evaluation of the marker coverage is important in understanding how to design an effective genomic research study with reasonable expectations about the power to obtain desired results. An important parameter to evaluate coverage is linkage disequilibrium, which can be used to determine the appropriate number of markers for a particular analysis and is related to the proportion of variance that can be explained by a given marker, or power. Finally, the type of analysis used to identify marker-trait associations may depend on marker coverage as the optimal approach, from a statistical or computational standpoint, may differ with changes in marker number and distribution.

Background Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.

Although common datasets are an important resource for the scientific community and can be used to address important questions, genomic datasets of a meaningful size have not generally been available in livestock species. We describe a pig dataset that PIC (a Genus company) has made available for comparing genomic prediction methods. We also describe genomic evaluation of the data using methods that PIC considers best practice for predicting and validating genomic breeding values, and we discuss the impact of data structure on accuracy. The dataset contains 3534 individuals with high-density genotypes, phenotypes, and estimated breeding values for five traits. Genomic breeding values were calculated using BayesB, with phenotypes and de-regressed breeding values, and using a single-step genomic BLUP approach that combines information from genotyped and un-genotyped animals. The genomic breeding value accuracy increased with increased trait heritability and with increased relationship between training and validation. In nearly all cases, BayesB using de-regressed breeding values outperformed the other approaches, but the single-step evaluation performed only slightly worse. This dataset was useful for comparing methods for genomic prediction using real data. Our results indicate that validation approaches accounting for relatedness between populations can correct for potential overestimation of genomic breeding value accuracies, with implications for genotyping strategies to carry out genomic selection programs.

Background There is wide interest in calculating genomic breeding values (GEBVs) in livestock using dense, genome-wide SNP data. The general framework for genomic selection assumes all individuals are genotyped at high-density, which may not be true in practice. Methods to add additional genotypes for individuals not genotyped at high density have the potential to increase GEBV accuracy with little or no additional cost. In this study a long haplotype library was created using a long range phasing algorithm and used in combination with segregation analysis to impute dense genotypes for non-genotyped dams in the training dataset (S1) and for non-genotyped or low-density genotyped individuals in the prediction dataset (S2), using the 14th QTL-MAS Workshop dataset. Alternative low-density scenarios were evaluated for accuracy of imputed genotypes and prediction of GEBVs. Results In S1, females in the training population were not genotyped and prediction individuals were either not genotyped or genotyped at low-density (evenly spaced at 2, 5 or 10 Mb). The proportion of correctly imputed genotypes for training females did not change when genotypes were added for individuals in the prediction set whereas the number of correctly imputed genotypes in the prediction set increased slightly (S1). The S2 scenario assumed the complete training set was genotyped for all SNPs and the prediction set was not genotyped or genotyped at low-density. The number of correctly imputed genotypes increased with genotyping density in the prediction set. Accuracy of genomic breeding values for the prediction set in each scenario were the correlation of GEBVs with true breeding values and were used to evaluate the potential loss in accuracy with reduced genotyping. For both S1 and S2 the GEBV accuracies were similar when the prediction set was not genotyped and increased with the addition of low-density genotypes, with the increase larger for S2 than S1. Conclusions Genotype imputation using a long haplotype library and segregation analysis is promising for application in sparsely-genotyped pedigrees. The results of this study suggest that dense genotypes can be imputed for selection candidates with some loss in genomic breeding value accuracy, but with levels of accuracy higher than traditional BLUP estimated breeding values. Accurate genotype imputation would allow for a single low-density SNP panel to be used across traits.

With the monthly bill provided by the energy company, a homeowner is given the total amount energy consumed in the home. This is, however, a comprehensive sum and does not inform the homeowner which devices are using large amounts of energy. Data provided by the ecoMOD energy monitoring system show that the heating, ventilation, and air conditioning (HVAC) system consumes approximately fifty percent of the total energy required in the home. Additionally, a significant portion of the HVAC system's energy consumption occurs during the periods of time in which the house is unoccupied and the HVAC system use could be minimized. To help limit this unnecessary use, the team is designing an automated thermostat that operates based on the house occupancy. Using a combination of carbon dioxide sensors and motion detectors to determine the occupancy of the house, the thermostat will adjust the temperature inside the home. When the homeowner reenters the home, the thermostat will return to the original set point. With such a system, the burden of constantly adjusting the thermostat to reflect changes in house occupancy will be removed. This will help to lower monthly bills, paving an avenue toward energy sustainability.

In recent years, an ensuing energy crisis has increased concern over home energy expenditures. Homeowners, through just their power meter or electricity bills, will know how much energy they consume over any given month. With just an energy bill, there is little to tell which devices in the home are consuming the most energy. Through the University of Virginia's ecoMOD project, several energy efficient houses have been designed and built. Low-income families now reside in these homes, allowing the project team to gather information on families who have limited financial ability to save energy. In an attempt to solve the problem, the ecoMOD project also included the installation of a monitoring system, which records energy data readings through special sensors. Through this monitoring data, the ecoMOD team performed analyses in order to both determine where a homeowner uses the most energy and to provide an energy-efficient solution geared towards lowering consumption. In addition, the team is in the process of producing a graphical user interface to give homeowners a detailed view where energy is most likely to be expended unnecessarily. This web application will be designed specifically to use the data gathered to encourage the homeowner to notice and take action upon ways they can save significant amounts of energy. This combination of detailed information feedback and customized energy solutions will give the ecoMOD homeowner tools they can use to make a conscious step towards energy sustainability.

Background Genome-wide approaches to analyze single nucleotide polymorphism (SNP) data have proliferated due to the increased availability and affordability of markers, but in practice a small number of markers may be selected from sets that do not approach dense genome-wide coverage. This study focused on a genome-wide approach to identify markers useful to a breeding program using a Bayesian method to estimate effects for markers distributed across the genome at varied densities. A simulated dataset containing 4665 individual phenotypes for a quantitative trait and genotypes for 6000 SNPs spaced in 0.1 cM increments across six chromosomes was analyzed using a Bayesian approach in which effects for all single markers are simultaneously estimated. The dataset was also analyzed with marker densities reduced to 0.5, 1.0, 2.0 and 5.0 cM. Type I errors were not a major concern but replications of each analysis were performed to determine acceptance of estimated marker effects. Results The Bayesian analysis of the original dataset was able to estimate genetic values for markers in a small number of regions while shrinking other marker effects to zero. Analysis of the reduced density datasets also showed clear signals in a small number of regions where some effects appeared to be distributed across multiple markers. Replicates of the analyses provided evidence for regions with moderate and large effects. Conclusion A Bayesian multiple marker approach appears to be suitable for predicting genetic values, even with reduced density datasets where large numbers of markers are not yet available for many species. These predicted genetic values can be implemented in marker assisted selection programs.

Background Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations. Methods Pedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated. Results Imputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP). Conclusions Among the selection candidates, 95.5 % and 93.5 % of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively, $24.74 and $20.58 per candidate.