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Implementation of genome-scale sequencing in clinical care has significant challenges: the technology is highly dimensional with many kinds of potential results, results interpretation and delivery require expertise and coordination across multiple medical specialties, clinical utility may be uncertain, and there may be broader familial or societal implications beyond the individual participant. Transdisciplinary consortia and collaborative team science are well poised to address these challenges. However, understanding the complex web of organizational, institutional, physical, environmental, technologic, and other political and societal factors that influence the effectiveness of consortia is understudied. We describe our experience working in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, a multi-institutional translational genomics consortium.
A key aspect of the CSER consortium was the juxtaposition of site-specific measures with the need to identify consensus measures related to clinical utility and to create a core set of harmonized measures. During this harmonization process, we sought to minimize participant burden, accommodate project-specific choices, and use validated measures that allow data sharing.
Identifying platforms to ensure swift communication between teams and management of materials and data were essential to our harmonization efforts. Funding agencies can help consortia by clarifying key study design elements across projects during the proposal preparation phase and by providing a framework for data sharing data across participating projects.
In summary, time and resources must be devoted to developing and implementing collaborative practices as preparatory work at the beginning of project timelines to improve the effectiveness of research consortia.
Passive surveillance for lyssaviruses in UK bats has been ongoing since 1987 and has identified 13 cases of EBLV-2 from a single species; Myotis daubentonii. No other lyssavirus species has been detected. Between 2005 and 2015, 10 656 bats were submitted, representing 18 species, creating a spatially and temporally uneven sample of British bat fauna. Uniquely, three UK cases originate from a roost at Stokesay Castle in Shropshire, England, where daily checks for grounded and dead bats are undertaken and bat carcasses have been submitted for testing since 2007. Twenty per cent of Daubenton's bats submitted from Stokesay Castle since surveillance began, have tested positive for EBLV-2. Phylogenetic analysis reveals geographical clustering of UK viruses. Isolates from Stokesay Castle are more closely related to one another than to viruses from other regions. Daubenton's bats from Stokesay Castle represent a unique opportunity to study a natural population that appears to maintain EBLV-2 infection and may represent endemic infection at this site. Although the risk to public health from EBLV-2 is low, consequences of infection are severe and effective communication on the need for prompt post-exposure prophylaxis for anyone that has been bitten by a bat is essential.
Campylobacter is a common cause of intestinal disease in humans and is often linked to the consumption of contaminated poultry meat. Despite considerable research on the topic there is a large amount of uncertainty associated with Campylobacter epidemiology. A Bayesian model framework was applied to multiple longitudinal datasets on Campylobacter infection in UK broiler flocks to estimate the time at which each flock was first infected with Campylobacter. The model results suggest that the day of first infection ranges from 10 to 45 days; however, over half had a time of infection between 30 and 35 days. When considering only those flocks which were thinned, 48% had an estimated day of infection within 2 days of the day of thinning, thus suggesting an association between thinning and Campylobacter infection. These results demonstrate how knowledge of the time of infection can be correlated to known events to identify potential risk factors for infection.
The cumulative effect of co-infections between pathogen pairs on the haematological response of East African Short-horn Zebu calves is described. Using a longitudinal study design a stratified clustered random sample of newborn calves were recruited into the Infectious Diseases of East African Livestock (IDEAL) study and monitored at 5-weekly intervals until 51 weeks of age. At each visit samples were collected and analysed to determine the infection status of each calf as well as their haematological response. The haematological parameters investigated included packed cell volume (PCV), white blood cell count (WBC) and platelet count (Plt). The pathogens of interest included tick-borne protozoa and rickettsias, trypanosomes and intestinal parasites. Generalized additive mixed-effect models were used to model the infectious status of pathogens against each haematological parameter, including significant interactions between pathogens. These models were further used to predict the cumulative effect of co-infecting pathogen pairs on each haematological parameter. The most significant decrease in PCV was found with co-infections of trypanosomes and strongyles. Strongyle infections also resulted in a significant decrease in WBC at a high infectious load. Trypanosomes were the major cause of thrombocytopenia. Platelet counts were also affected by interactions between tick-borne pathogens. Interactions between concomitant pathogens were found to complicate the prognosis and clinical presentation of infected calves and should be taken into consideration in any study that investigates disease under field conditions.
Genome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects.
Although communicable diseases have hitherto played a small part in illness associated with Olympic Games, an outbreak of infection in a national team, Games venue or visiting spectators has the potential to disrupt a global sporting event and distract from the international celebration of athletic excellence. Preparation for hosting the Olympic Games includes implementation of early warning systems for detecting emerging infection problems. Ensuring capability for rapid microbiological diagnoses to inform situational risk assessments underpins the ability to dispel rumours. These are a prelude to control measures to minimize impact of any outbreak of infectious disease at a time of intense public scrutiny. Complex multidisciplinary teamwork combined with laboratory technical innovation and efficient information flows underlie the Health Protection Agency's preparation for the London 2012 Olympic and Paralympic Games. These will deliver durable legacies for clinical and public health microbiology, outbreak investigation and control in the coming years.
Genetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0·48% (T. colubriformis) or 0·08% (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0·32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.
Recently a paper authored by ourselves and a number of co-authors about the proportion of phenotypic variation in height that is explained by common SNPs was published in Nature Genetics (Yang et al., 2010). Common SNPs explain a large proportion of the heritability for human height (Yang et al.). During the refereeing process (the paper was rejected by two other journals before publication in Nature Genetics) and following the publication of Yang et al. (2010) it became clear to us that the methodology we applied, the interpretation of the results and the consequences of the findings on the genetic architecture of human height and that for other traits such as complex disease are not well understood or appreciated. Here we explain some of these issues in a style that is different from the primary publication, that is, in the form of a number of comments and questions and answers. We also report a number of additional results that show that the estimates of additive genetic variation are not driven by population structure.
The patterns of linkage disequilibrium (LD) between dense polymorphic markers are shaped by the ancestral population history. It is therefore possible to use multilocus predictors of LD to infer past population history and to infer sharing of identical alleles in quantitative trait locus (QTL) studies. We develop a multilocus predictor of LD for pairs of haplotypes, which we term haplotype homozygosity (HHn): the probability that any two haplotypes share a given number of n adjacent identical markers or ‘runs of homozygosity’. Our method, based on simplified coalescence theory, accounts for recombination and mutation. We compare our HHn predictions, with HHn in simulated populations and with two published predictors of HHn. Our method performs consistently better across a range of population parameters, including populations with a severe bottleneck followed by expansion, compared to two published methods. We demonstrate that we can predict the pattern of HHn observed in dense single nucleotide polymorphisms (SNPs) genotyped in a cattle population, given appropriate historical changes in population size. Our method is practical for use with very large numbers of individuals and dense genome wide polymorphic DNA data. It has potential applications in inferring ancestral population history and QTL mapping studies.
We used a least absolute shrinkage and selection operator (LASSO) approach to estimate marker effects for genomic selection. The least angle regression (LARS) algorithm and cross-validation were used to define the best subset of markers to include in the model. The LASSO–LARS approach was tested on two data sets: a simulated data set with 5865 individuals and 6000 Single Nucleotide Polymorphisms (SNPs); and a mouse data set with 1885 individuals genotyped for 10 656 SNPs and phenotyped for a number of quantitative traits. In the simulated data, three approaches were used to split the reference population into training and validation subsets for cross-validation: random splitting across the whole population; random sampling of validation set from the last generation only, either within or across families. The highest accuracy was obtained by random splitting across the whole population. The accuracy of genomic estimated breeding values (GEBVs) in the candidate population obtained by LASSO–LARS was 0·89 with 156 explanatory SNPs. This value was higher than those obtained by Best Linear Unbiased Prediction (BLUP) and a Bayesian method (BayesA), which were 0·75 and 0·84, respectively. In the mouse data, 1600 individuals were randomly allocated to the reference population. The GEBVs for the remaining 285 individuals estimated by LASSO–LARS were more accurate than those obtained by BLUP and BayesA for weight at six weeks and slightly lower for growth rate and body length. It was concluded that LASSO–LARS approach is a good alternative method to estimate marker effects for genomic selection, particularly when the cost of genotyping can be reduced by using a limited subset of markers.
The potential dispersal of Benghal dayflower seeds by mourning doves was studied in southern Georgia, U.S.A. The gut contents (both crop and gizzard) of mourning doves harvested in the autumn months were investigated to determine if mourning doves fed on Benghal dayflower and whether seeds can survive conditions in the bird gut. Research indicated that mourning doves fed selectively on Benghal dayflower with some harvested birds containing hundreds of Benghal dayflower seeds and capsules in their guts. Further, some seeds recovered remained highly viable. Germination rates in seeds taken from bird crops were similar to controls over the first 4 wk of germination and enhanced over control treatments during the latter 16 wk of a 20-wk germination study. Ultimately, seeds extracted from dove crops had 92% germination as compared to 80% for control seeds. Seeds extracted from dove gizzards had 45% germination, about half that of controls. Benghal dayflower seeds have a structurally reinforced seed coat that probably aids in survival of mechanical damage through bird intestinal tracts. Benghal dayflower seeds exposed to 1.0 M HCl treatment for 2 h had little loss in viability, successfully germinating after such treatment. When evaluating mechanisms for the eradication of Benghal dayflower from agricultural crops, consideration needs to be given to the large number of mourning doves and other bird species that visit cropland and potentially aid in its dispersal.
Rotaviruses present in products of wastewater treatment were assayed in MA 104 cells by indirect immunofluorescence. Levels in settled sewage, activated sludge and effluent were greater than 103 per litre in March and April but virus was not detected during later months. This pattern correlated with the decline in laboratory reports of human rotavirus infection.
Sera from 218 of 1574 (14%) small mammals collected in the Yukon Territory between 14 May and 13 August 1972 neutralized a Yukon strain of California encephalitis virus (snowshoe-hare subtype). These included 133 of 319 (42%) snowshoe hares (Lepus americanus), 84 of 1243 (7%) ground squirrels (Citellus undulatus) and 1 of 12 (8%) tree squirrels (Tamiasciurus hudsonicus). California encephalitis virus (snow-shoe hare subtype) was isolated from four pools of unengorged Aedes communis mosquitoes collected near Whitehorse (61° N., 135° W.) and on one occasion each from pools of the same species collected at Hunker Creek (64° N., 138° W.) and at mile 125, Dempster Highway (66° N., 138° W.) during July 1972. Replication of a Yukon strain of California encephalitis virus was observed in wild-caught Culiseta inornata and Aedes canadensis mosquitoes after intrathoracic injection and holding at temperatures of 80°, 50° and 40° F.
By means of techniques of analyses of survival data developed for cancer trials it is possible to study aspects of the natural history of the infection of schistosomiasis on the intermediate host of transmission, the snail.
The simultaneous study of three response variables is largely based on a model of Lagakos (1976). When using this approach in the schistosomiasis setting it seems inappropriate to assume that one process, the duration of latency, follows an exponential distribution. Thus this stage is modified to follow a normal distribution and the derivatives required to obtain maximum-likelihood estimates and approximate variances of all parameters are provided.
Simple graphical tools for assessing the validity of distributional assumptions in survival data are available from industrial research. The reader's attention is drawn to a paper by Nelson (1972). The relevance and application of these methods to the current problem are described in Section 4.
In the event that the times to death of prepatent and patent snails do not follow exponential distributions as assumed in the primary model, a further modification is introduced to enable either or both to follow Weibull densities.
Lastly it is possible to adapt both the primary model of Section Three and the modified model of Section Five to allow for the inclusion of auxiliary variables or covariates. Again the required derivatives to obtain maximum likelihood estimates and approximate variances are provided.
I wish to thank Sheila Gore, Stuart Pocock and Professor Michael Healy for many useful discussions. Sheila Gore and Professor Peter Armitage provided many recommendations for the improvement of the manuscript for which I am most grateful.