Biology subjects

Hanscombe, K. B.

Publications and source records attributed to Hanscombe, K. B..

3 recordsLinked to original sources

bioRxiv2018

Navigome: Navigating the Human Phenome

We now have access to a sufficient number of genome-wide association studies (GWAS) to cluster phenotypes into genetic-informed categories and to navigate the \"phenome\" space of human traits. Using a collection of 465 GWAS, we generated genetic correlations, pathways, gene-wise and tissue-wise associations using MAGMA and S-PrediXcan for 465 human traits. Testing 7267 biological pathways, we found that only 898 were significantly associated with any trait. Similarly, out of ~20,000 tested protein-coding genes, 12,311 genes exhibited an association. Based on the genetic correlations between all traits, we constructed a phenome map using t-distributed stochastic neighbor embedding (t-SNE), where each of the 465 traits can be visualized as an individual point. This map reveals well-defined clusters of traits such as education/high longevity, lower longevity, height, body composition, and depression/anxiety/neuroticism. These clusters are enriched in specific groups of pathways, such as lipid pathways in the lower longevity cluster, and neuronal pathways for body composition or education clusters. The map and all other analyses are available in the Navigome web interface (https://phenviz.navigome.com).

bioinformatics↗

bioRxiv2017

Predictors of intestinal inflammation in asymptomatic first-degree relatives of patients with Crohn’s disease

ObjectiveRelatives of individuals with Crohns disease (CD) carry an increased number of CD-associated genetic variants and are at increased risk of developing the disease. Multiple environmental and genetic factors contribute to this increased risk. We aimed to estimate the utility of genotype, smoking, family history, and a panel of biomarkers to predict risk in asymptomatic first-degree relatives (FDRs) of CD patients.\n\nDesignWe calculated a combined genotype (72 CD-associated genetic markers) and smoking relative risk score in 454 FDRs, and performed capsule endoscopy and collected 22 biomarkers in individuals from the highest and lowest risk quartiles. We then predicted small intestinal inflammation using genetic risk score, smoking status, number of relatives with CD, capsule transit time, and the panel of biomarkers in 124 individuals with complete data. Our principal analysis was to calculate the predictive utility from two machine learning classifiers: an elastic net and a random forest.\n\nResultsBoth classifiers successfully predicted FDRs with intestinal inflammation: elastic net (AUC=0.80, 95% CI: 0.62-0.98), random forest (AUC=0.87, 95% CI: 0.75-1.00). The elastic net selected a 3-predictor solution: CD family history (OR=1.31), genetic risk score (OR=1.14), and faecal calprotectin (OR=1.04). The same 3 variables were among the top 5 most important predictors as ranked by the random forest.\n\nConclusionA readily collectable panel of genetic risk variants, added to family history and faecal calprotectin, predicts those at greatest risk for developing CD with a good degree of accuracy.

epidemiology↗

bioRxiv2017

ukbtools: An R package to manage and query UK Biobank data

SummaryThe UK Biobank is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. ukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis, and provides tools to assist in quality control, query of disease diagnoses, and retrieval of genetic metadata.\n\nAvailabilityThe package is available for installation from the Comprehensive R Archive Network (CRAN), and includes a vignette describing the use of all functionality.\n\nContact ken.b.hanscombe@kcl.ac.uk, https://github.com/kenhanscombe/ukbtools

epidemiology↗