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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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Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes.

TL;DR: A pvalue-assisted subset testing for associations (pASTA) framework that generalizes the previously proposed association analysis based on subsets (ASSET) method by incorporating gene-environment (G-E) interactions into the testing procedure and allows researchers to determine the most probable subset of traits that exhibit genetic associations in addition to the enhancement of power.
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Point and Interval Estimates of Marker Location in Radiation Hybrid Mapping

TL;DR: This work proposes a statistical method for estimating marker position that combines information from all plausible marker orders, gives a measure of uncertainty in location for each marker, and provides an alternative to the current practice of binning.
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Localization of the Homolog of a Mouse Craniofacial Mutant to Human Chromosome 18q11 and Evaluation of Linkage to Human CLP and CPO

TL;DR: The transgene-induced mutation 9257 and the spontaneous mutation twirler cause craniofacial and inner ear malformations and are located on mouse chromosome 18 near the ataxia locus ax and the human homolog of 9257 was mapped by linkage analysis using the CEPH pedigrees.
Posted ContentDOI

Multi-SKAT: General framework to test for rare variant association with multiple phenotypes

TL;DR: A general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT), which can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.