Jeffrey R. O'Connell

Jeffrey R. O'Connell, DPhil is an Associate Professor in the Department of Medicine at the University of Maryland School of Medicine, with a secondary appointment in Epidemiology & Public Health. He has held his position at the University of Maryland since 2002, initially as an Assistant Professor (2002-2008) before being promoted to Associate Professor in 2009, a position he continues to hold. Additionally, he maintains a research appointment at the USDA Animal Genetics Improvement Laboratory where he has worked since 2007.

Dr. O'Connell's educational background includes a BS in Mathematics from the University of Delaware (1979), an MS in Mathematics from Drexel University (1984), an MS in Computer Science from Drexel University (1993), and a D.Phil in Mathematical Biology from Oxford University (2000). His academic journey reflects a strong foundation in mathematics and computational methods that underpins his research career.

Dr. O'Connell's research focuses on developing, implementing and applying methods to analyze genomic data in large pedigree and population data. His primary area has been human genetics, which expanded to animal genetics in 2007 with his appointment at the USDA Animal Improvement Laboratory. He is the developer of MMAP (Mixed Models for Analysis of Pedigree/Populations), a comprehensive software package implementing analysis options for genome-wide association, variance components estimation, linkage analysis, genotype imputation, genomic prediction, haplotyping, and mega analysis. His work extensively utilizes large pedigree data such as the Old Order Amish and Holstein cattle.

Dr. O'Connell collaborates with numerous research consortia including the Genetics of Liver Disease (GOLD), the Genetic Factors for Osteoporosis (GEFOS), the Cohorts of Heart and Aging Research in Genetic Epidemiology (CHARGE) musculoskeletal, adiposity, and lipid groups, and the Gene-by-Lifestyle Interaction consortium. He is also a member of the TOPMed whole genome sequencing effort and has been heavily involved in developing analytical tools for cloud-based computing to run genome-wide association and rare variant analysis, generalized linear mixed models for binary and threshold traits, and multi- and correlated trait models for analysis of large omics data sets.

His research has resulted in numerous high-impact publications spanning statistical genetics, genomic analysis methods, and applications to both human and animal genetics. His work bridges computational methodology development with practical applications in genetic epidemiology and animal breeding.