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Jeffrey R. O'Connell

Researcher at University of Maryland, Baltimore

Publications -  11
Citations -  2492

Jeffrey R. O'Connell is an academic researcher from University of Maryland, Baltimore. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 7, co-authored 11 publications receiving 2226 citations. Previous affiliations of Jeffrey R. O'Connell include University of Maryland, Baltimore County & Johns Hopkins University.

Papers
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Defining the role of common variation in the genomic and biological architecture of adult human height

Andrew R. Wood, +444 more
- 01 Nov 2014 - 
TL;DR: This article identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height, and all common variants together captured 60% of heritability.
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Genome-Wide Association Study for Coronary Artery Calcification With Follow-Up in Myocardial Infarction

Christopher J. O'Donnell, +71 more
- 20 Dec 2011 - 
TL;DR: SNPs in the 9p21 and PHACTR1 gene loci were strongly associated with CAC and MI, and there is suggestive associations with both Cac and MI of SNPs in additional loci.
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Deep-coverage whole genome sequences and blood lipids among 16,324 individuals.

TL;DR: Large-scale deep-coverage whole-genome sequencing is now feasible and offers potential advantages for locus discovery and the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia.
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The genetic response to short-term interventions affecting cardiovascular function: rationale and design of the Heredity and Phenotype Intervention (HAPI) Heart Study.

TL;DR: Identifying these response genes may identify new mechanisms influencing CVD and may lead to individualized preventive strategies and improved early detection of high-risk individuals.
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Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges

TL;DR: The challenges associated with harmonizing alcohol-consumption data from studies with widely different assessment instruments are discussed, with a particular focus on large-scale genetic studies.