Mendelian pathway analysis of laboratory traits reveals distinct roles for ciliary subcompartments in common disease pathogenesis.

Rare monogenic disorders of the primary cilium, termed ciliopathies, are characterized by extreme presentations of otherwise common diseases, such as diabetes, hepatic fibrosis, and kidney failure. However, despite a recent revolution in our understanding of the cilium's role in rare disease pathogenesis, the organelle's contribution to common disease remains largely unknown. Hypothesizing that common genetic variants within Mendelian ciliopathy genes might contribute to common complex diseases pathogenesis, we performed association studies of 16,874 common genetic variants across 122 ciliary genes with 12 quantitative laboratory traits characteristic of ciliopathy syndromes in 452,593 individuals in the UK Biobank. We incorporated tissue-specific gene expression analysis, expression quantitative trait loci, and Mendelian disease phenotype information into our analysis and replicated our findings in meta-analysis. 101 statistically significant associations were identified across 42 of the 122 examined ciliary genes (including eight novel replicating associations). These ciliary genes were widely expressed in tissues relevant to the phenotypes being studied, and eQTL analysis revealed strong evidence for correlation between ciliary gene expression levels and laboratory traits. Perhaps most interestingly, our analysis identified different ciliary subcompartments as being specifically associated with distinct sets of phenotypes. Taken together, our data demonstrate the utility of a Mendelian pathway-based approach to genomic association studies, challenge the widely held belief that the cilium is an organelle important mainly in development and in rare syndromic disease pathogenesis, and provide a framework for the continued integration of common and rare disease genetics to provide insight into the pathophysiology of human diseases of immense public health burden.

Genome-wide and candidate gene approaches of clopidogrel efficacy using pharmacodynamic and clinical end points-Rationale and design of the International Clopidogrel Pharmacogenomics Consortium (ICPC).

RATIONALE: The P2Y12 receptor inhibitor clopidogrel is widely used in patients with acute coronary syndrome, percutaneous coronary intervention, or ischemic stroke. Platelet inhibition by clopidogrel shows wide interpatient variability, and high on-treatment platelet reactivity is a risk factor for atherothrombotic events, particularly in high-risk populations. CYP2C19 polymorphism plays an important role in this variability, but heritability estimates suggest that additional genetic variants remain unidentified. The aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify genetic determinants of clopidogrel pharmacodynamics and clinical response. STUDY DESIGN: Based on the data published on www.clinicaltrials.gov, clopidogrel intervention studies containing genetic and platelet function data were identified for participation. Lead investigators were invited to share DNA samples, platelet function test results, patient characteristics, and cardiovascular outcomes to perform candidate gene and genome-wide studies. RESULTS: In total, 17 study sites from 13 countries participate in the ICPC, contributing individual patient data from 8,829 patients. Available adenosine diphosphate-stimulated platelet function tests included vasodilator-stimulated phosphoprotein assay, light transmittance aggregometry, and the VerifyNow P2Y12 assay. A proof-of-principle analysis based on genotype data provided by each group showed a strong and consistent association between CYP2C19*2 and platelet reactivity (P value=5.1 × 10-40). CONCLUSION: The ICPC aims to identify new loci influencing clopidogrel efficacy by using state-of-the-art genetic approaches in a large cohort of clopidogrel-treated patients to better understand the genetic basis of on-treatment response variability.

Informatics and machine learning to define the phenotype.

INTRODUCTION: For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

A biologically informed method for detecting rare variant associations.

BACKGROUND: BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions (ECRs), protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting. PURPOSE OF THE STUDY: In this manuscript, we describe the software package for BioBin, along with type I error and power simulations to demonstrate the strengths and various customizable features and analysis options of this variant binning tool. RESULTS: Simulation testing highlights the utility of BioBin as a fast, comprehensive and expandable tool for the biologically-inspired binning and analysis of low-frequency variants in sequence data. CONCLUSIONS AND POTENTIAL IMPLICATIONS: The BioBin software package has the capability to transform and streamline the analysis pipelines for researchers analyzing rare variants. This automated bioinformatics tool minimizes the manual effort of creating genomic regions for binning such that time can be spent on the much more interesting task of statistical analyses. This software package is open source and freely available from http://ritchielab.com/software/biobin-download.