What is the state of children\u27s participation in qualitative research on health interventions?: A scoping study

BACKGROUND: Children are the focus of numerous health interventions throughout the world, yet the extent of children\u27s meaningful participation in research that informs the adaptation, implementation, and evaluation of health interventions is not known. We examine the type, extent, and meaningfulness of children\u27s participation in research in qualitative health intervention research. METHOD: A scoping study was conducted of qualitative published research with children (ages 6-11 years) carried out as part of health intervention research. Following Arksey and O\u27Malley\u27s scoping study methodology and aligned with the PRISMA-ScR guidelines on the reporting of scoping reviews, the authors searched, charted, collated, and summarized the data, and used descriptive and content analysis techniques. Ovid MEDLINE was searched from 1 January 2007 to 2 July 2018 using the keywords children, health intervention, participation, and qualitative research. Study selection and data extraction were carried out by two reviewers independently. RESULTS: Of 14,799 articles screened, 114 met inclusion criteria and were included. The study identified trends in when children were engaged in research (e.g., post-implementation rather than pre-implementation), in topical (e.g., focus on lifestyle interventions to prevent adult disease) and geographical (e.g., high-income countries) focuses, and in qualitative methods used (e.g., focus group). While 78 studies demonstrated meaningful engagement of children according to our criteria, there were substantial reporting gaps and there was an emphasis on older age (rather than experience) as a marker of capability and expertise. CONCLUSIONS: Despite evidence of children\u27s meaningful participation, topical, geographical, and methodological gaps were identified, as was the need to strengthen researchers\u27 skills in interpreting and representing children\u27s perspectives and experiences. Based on these findings, the authors present a summary reflective guide to support researchers toward more meaningful child participation in intervention research

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease