Tom Gaunt | bioRxiv Science

bioRxiv2016

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

MotivationLD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.\n\nResultsIn this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 177 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.\n\nAvailability and implementationThe web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

Bioinformatics↗

bioRxiv2015

Diagnosis of coronary heart diseases using gene expression profiling; stable coronary artery disease, cardiac ischemia with and without myocardial necrosis

Cardiovascular disease including coronary artery disease and myocardial infarction is one of the leading causes of death in Europe, and is influenced by both environmental and genetic factors. With the advancements in genomic tools and technologies there is potential to predict and diagnose heart disease using molecular data from analysis of blood cells. We analyzed gene expression data from blood samples taken from normal people (n=21), non-significant coronary artery disease (n=93), patients with unstable angina (n=16), stable coronary artery disease (n=14) and myocardial infarction (MI; n=207). We used a feature selection approach to identify a set of gene expression variables which successfully differentiate different cardiovascular diseases. The initial features were discovered by fitting a linear model for each probe set across all arrays of normal individuals and patients with myocardial infarction. Three different feature optimisation algorithms were devised which identified two most discriminating sets of genes one using MI and normal controls (total genes=8) and another one using MI and unstable angina patients (total genes=17). The results proved the diagnostic robustness of the final feature sets in discriminating not only patients with myocardial infraction from healthy controls but also from patients with clinical symptoms of cardiac ischemia with myocardial necrosis and stable coronary artery disease despite the influence of batch effects and different microarray gene chips and platforms. selection approach to identify a set of gene expression variables which successfully differentiate different cardiovascular diseases. The initial features were discovered by fitting a linear model for each probe set across all arrays of normal individuals and patients with myocardial infarction. Three different feature optimisation algorithms were devised which identified two most discriminating sets of genes one using MI and normal controls (total genes=8) and another one using MI and unstable angina patients (total genes=17). The results proved the diagnostic robustness of the final feature sets in discriminating not only patients with myocardial infraction from healthy controls but also from patients with clinical symptoms of cardiac ischemia with myocardial necrosis and stable coronary artery disease despite the influence of batch effects and different microarray gene chips and platforms.

Bioinformatics↗