John Barnard Research Program

John Barnard, PhD

Associate Staff
Email: [email protected]
Location: Cleveland Clinic Main Campus

Research

Dr. John Barnard's extensive collaborations with physician researchers, translational and basic scientists at the Cleveland Clinic over the past 15 years have focused on understanding the pathogenesis and etiology of various diseases, including atrial fibrillation (AF), pulmonary vascular disease (PVD), coronary artery disease (CAD) and myelodysplastic syndromes (MDS). In the AF, PVD, CAD and MDS projects, his lab is using an integrated omics approach, including targeted next generation DNA sequencing, whole genome and exome sequencing, mRNA-seq, miRNA-seq, DNA methylation-seq, microarray expression and SNP chips, to elucidate the roles and interconnections of the genome, transcriptome, epigenome, metabolome anddiseasome. Their approach takes high-dimensional data from eachomiclayer and attempts to link them using methods suitable for big data, such as co-expression networks, trans and cis eQTLusing machine learning regression, genome-wide allelic expression imbalance analysis and sparse clustering.  

Biography

Dr. Barnard is the Section Head for two sections, Biostatistics and Statistical Genetics/Bioinformatics, within the Department of Quantitative Health Sciences at Cleveland Clinic. He has over 17 years of experience in a wide variety of applications, grants and methods development involving bioinformatics and genomics. In addition to my collaborative work, Dr. Barnard has mentored and trained several students from the Cleveland Clinic Lerner College of Medicine and the Case/Clinic’s Molecular Medicine PhD program. Several of these students have worked with him on projects involving big data and have won training scholarships. He has a strong track record of external funding and grant collaboration, serving as the Biostatistics Core Leader for two NIH SCCOR grants and a Program Project grant. Dr. Barnard is also a Principal Investigator of a NIH R01 grant on AF functional genomics and transcriptomics, lead the Data Analysis Cores for a large consortium grant on MDS, an AHA research network grant on AF progression and a U01 on PVD Omics. He has collaborated with Dr. Nazha on several MDS-related projects and would expand this collaboration and develop new collaborations through the proposed CCF/LRI COE for Artificial Intelligence.

Education & Professional Highlights