M
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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Journal ArticleDOI
Autosomal Dominant Nanophthalmos (NNO1) with High Hyperopia and Angle-Closure Glaucoma Maps to Chromosome 11
Mohammad Othman,S. A. Sullivan,Gregory L. Skuta,D. A. Cockrell,Heather M. Stringham,Catherine A. Downs,A. Fornés,A. Mick,Michael Boehnke,Douglas Vollrath,Julia E. Richards +10 more
TL;DR: Clinical and genetic evaluations of members of a large family in which nanophthalmos is transmitted in an autosomal dominant manner demonstrated highly significant evidence that nanophilethalmos in this family is the result of a defect in a previously unidentified locus (NNO1) on chromosome 11.
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Recent advances in understanding the genetic architecture of type 2 diabetes
Karen L. Mohlke,Michael Boehnke +1 more
TL;DR: Genome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c.
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Exploring and visualizing large-scale genetic associations by using PheWeb
Sarah A Gagliano Taliun,Peter VandeHaar,Andrew P. Boughton,Ryan P. Welch,Daniel Taliun,Ellen M. Schmidt,Wei Zhou,Jonas B. Nielsen,Cristen J. Willer,Seunggeun Lee,Lars G. Fritsche,Michael Boehnke,Gonçalo R. Abecasis,Gonçalo R. Abecasis +13 more
TL;DR: PheWeb is an easy-to-use open-source web-based tool for visualizing, navigating and sharing GWAS and PheWAS results, used to explore association results for large datasets such as the UK Biobank5 and the Michigan Genomics Initiative, and organizes relationships between traits on the basis of pairwise genetic correlations.
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Comprehensive Association Study of Type 2 Diabetes and Related Quantitative Traits With 222 Candidate Genes
Kyle J. Gaulton,Cristen J. Willer,Yun Li,Laura J. Scott,Karen N. Conneely,Anne U. Jackson,William L. Duren,Peter S. Chines,Narisu Narisu,Lori L. Bonnycastle,Jingchun Luo,Maurine Tong,Andrew G. Sprau,Elizabeth W. Pugh,Kimberly F. Doheny,Timo T. Valle,Gonçalo R. Abecasis,Jaakko Tuomilehto,Jaakko Tuomilehto,Richard N. Bergman,Francis S. Collins,Michael Boehnke,Karen L. Mohlke +22 more
TL;DR: This study provides an effective gene-based approach to association study design and analysis and implicated novel genes, including RAPGEF1 and TP53, in type 2 diabetes susceptibility.
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Efficient Study Designs for Test of Genetic Association Using Sibship Data and Unrelated Cases and Controls
TL;DR: The likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP is extended to accommodate sibships of arbitrary size and disease-phenotype configuration, suggests that when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient.