Identification of recurrent regions of copy-number variants across multiple individuals

10.1186/1471-2105-11-147BMC Bioinformatics1

Integrating new assessment strategies into mathematics classrooms: an exploratory study in Singapore primary and secondary schools

Educational researchers and practitioners have in recent years paid mounting attention to the importance of new assessment (or the so-called alternative assessment) strategies in Mathematics instruction to better reflect the new desired educational goals and shifted values in education. However, research is wanting in this area, particularly in Singapore's educational setting. This project seeks to investigate the influence of using new assessment strategies in Mathematics teaching and learning on students' achievements, in both the cognitive and affective domains, in our local school settings. A quasi-experimental study with about 15-20 teachers at primary and lower secondary levels will be carried out to assess the impact of using a variety of strategies (e.g., projects, journal writing, oral presentation, performance tasks, student self-assessment, classroom observation and interview, etc.) for three school semesters on students' learning. The project will also look into issues concerning how to use new assessment strategies effectively in classrooms in local schools. For this purpose, data will be collected from classroom observation, interviews with teachers and students, and questionnaire surveys. It is hoped that the project will provide research-based evidence and practical suggestions for promoting the effective use of alternative assessment in Singapore Mathematics classrooms. <br/

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting

The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with -15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases.Peer reviewe

Airway Microbiota Dynamics Uncover a Critical Window for Interplay of Pathogenic Bacteria and Allergy in Childhood Respiratory Disease.

Repeated cycles of infection-associated lower airway inflammation drive the pathogenesis of persistent wheezing disease in children. In this study, the occurrence of acute respiratory tract illnesses (ARIs) and the nasopharyngeal microbiome (NPM) were characterized in 244 infants through their first five years of life. Through this analysis, we demonstrate that >80% of infectious events involve viral pathogens, but are accompanied by a shift in the NPM toward dominance by a small range of pathogenic bacterial genera. Unexpectedly, this change frequently precedes the detection of viral pathogens and acute symptoms. Colonization of illness-associated bacteria coupled with early allergic sensitization is associated with persistent wheeze in school-aged children, which is the hallmark of the asthma phenotype. In contrast, these bacterial genera are associated with "transient wheeze" that resolves after age 3 years in non-sensitized children. Thus, to complement early allergic sensitization, monitoring NPM composition may enable early detection and intervention in high-risk children

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus

Neonatal genetics of gene expression reveal potential origins of autoimmune and allergic disease risk

Abstract: Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4+ T cells from cord blood samples, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis-eQTLs are largely specific to cell type or stimulation, and 31% and 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectively. We identified cis regulatory factors acting as mediators of trans effects. There is extensive colocalisation between condition-specific neonatal cis-eQTLs and variants associated with immune-mediated diseases, in particular CTSH had widespread colocalisation across diseases. Mendelian randomisation shows causal neonatal gene expression effects on disease risk for BTN3A2, HLA-C and others. Our study elucidates the genetics of gene expression in neonatal immune cells, and aetiological origins of autoimmune and allergic diseases

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting

The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with -15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases

Trajectories of childhood immune development and respiratory health relevant to asthma and allergy.

Events in early life contribute to subsequent risk of asthma; however, the causes and trajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly, not all atopic individuals develop wheeze, and vice versa. The reasons for these differences are unclear. Using unsupervised model-based cluster analysis, we identified latent clusters within a prospective birth cohort with deep immunological and respiratory phenotyping. We characterised each cluster in terms of immunological profile and disease risk, and replicated our results in external cohorts from the UK and USA. We discovered three distinct trajectories, one of which is a high-risk 'atopic' cluster with increased propensity for allergic diseases throughout childhood. Atopy contributes varyingly to later wheeze depending on cluster membership. Our findings demonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthma pathogenesis and provide a foundation for improving management and prevention of childhood asthma