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Michael Boehnke

Researcher at University of Michigan

Publications -  540
Citations -  155551

Michael Boehnke is an academic researcher from University of Michigan. The author has contributed to research in topics: Genome-wide association study & Type 2 diabetes. The author has an hindex of 152, co-authored 511 publications receiving 136681 citations. Previous affiliations of Michael Boehnke include SUNY Downstate Medical Center & Norwegian University of Science and Technology.

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Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Anne E. Justice, +370 more
TL;DR: The results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution, and highlight the importance of accounting for environment in genetic analyses.
Posted ContentDOI

The Trans-Ancestral Genomic Architecture of Glycaemic Traits

Ji Chen, +478 more
- 25 Jul 2020 - 
TL;DR: Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways, increasing understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.
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Meta-analysis of genome-wide linkage studies in BMI and obesity.

TL;DR: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI‐defined obesity using a nonparametric genome scan meta‐analysis.
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The Finland–United States Investigation of Non–Insulin-Dependent Diabetes Mellitus Genetics (FUSION) Study. II. An Autosomal Genome Scan for Diabetes-Related Quantitative-Trait Loci

TL;DR: Several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population are identified by integrating genome-scan results from the companion article by Ghosh et al.

Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk

Helen R. Warren, +267 more
TL;DR: Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure–raising genetic variants on future cardiovascular disease risk.