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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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Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

Goo Jun, +84 more
TL;DR: The results from deep whole-genome analysis of large Mexican-American pedigrees are described, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families.
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LASER server: ancestry tracing with genotypes or sequence reads

TL;DR: The LASER server is described to facilitate application of the method to a wide range of genetic studies and provides genetic ancestry estimation for different geographic regions and user‐friendly interactive visualization of the results.
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A multi-ancestry genome-wide study incorporating gene–smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure

Yun Ju Sung, +317 more
TL;DR: A genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stages 2 identified 136 loci significantly associated with MAP and/or PP and identified nine new signals near known loci.
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Simulation of Finnish Population History, Guided by Empirical Genetic Data, to Assess Power of Rare-Variant Tests in Finland

Sophie R. Wang, +89 more
TL;DR: It is demonstrated that power of rare-variant association tests is higher in the Finnish population, especially when variants' phenotypic effects are tightly coupled with fitness effects and therefore reflect a greater contribution of rarer variants.
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Gene level meta-analysis of quantitative traits by functional linear models

TL;DR: Functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates, and related test statistics can be useful in whole-genome and whole-exome association studies.