Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. METHODS: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children. FINDINGS: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures. CONCLUSIONS: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. FUNDING: European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC

A Latent Variable Partial Least Squares Path Modeling Approach to Regional Association and Polygenic Effect with Applications to a Human Obesity Study

Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609∼rs9939881 at the first intron P = 4.29×10−7) than single SNP analysis (all with P>10−4) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk (N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits

Life-threatening infections in children in Europe (the EUCLIDS Project): a prospective cohort study

Background: Sepsis and severe focal infections represent a substantial disease burden in children admitted to hospital. We aimed to understand the burden of disease and outcomes in children with life-threatening bacterial infections in Europe. Methods: The European Union Childhood Life-threatening Infectious Disease Study (EUCLIDS) was a prospective, multicentre, cohort study done in six countries in Europe. Patients aged 1 month to 18 years with sepsis (or suspected sepsis) or severe focal infections, admitted to 98 participating hospitals in the UK, Austria, Germany, Lithuania, Spain, and the Netherlands were prospectively recruited between July 1, 2012, and Dec 31, 2015. To assess disease burden and outcomes, we collected demographic and clinical data using a secured web-based platform and obtained microbiological data using locally available clinical diagnostic procedures. Findings: 2844 patients were recruited and included in the analysis. 1512 (53·2%) of 2841 patients were male and median age was 39·1 months (IQR 12·4–93·9). 1229 (43·2%) patients had sepsis and 1615 (56·8%) had severe focal infections. Patients diagnosed with sepsis had a median age of 27·6 months (IQR 9·0–80·2), whereas those diagnosed with severe focal infections had a median age of 46·5 months (15·8–100·4; p<0·0001). Of 2844 patients in the entire cohort, the main clinical syndromes were pneumonia (511 [18·0%] patients), CNS infection (469 [16·5%]), and skin and soft tissue infection (247 [8·7%]). The causal microorganism was identified in 1359 (47·8%) children, with the most prevalent ones being Neisseria meningitidis (in 259 [9·1%] patients), followed by Staphylococcus aureus (in 222 [7·8%]), Streptococcus pneumoniae (in 219 [7·7%]), and group A streptococcus (in 162 [5·7%]). 1070 (37·6%) patients required admission to a paediatric intensive care unit. Of 2469 patients with outcome data, 57 (2·2%) deaths occurred: seven were in patients with severe focal infections and 50 in those with sepsis. Interpretation: Mortality in children admitted to hospital for sepsis or severe focal infections is low in Europe. The disease burden is mainly in children younger than 5 years and is largely due to vaccine-preventable meningococcal and pneumococcal infections. Despite the availability and application of clinical procedures for microbiological diagnosis, the causative organism remained unidentified in approximately 50% of patients

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe