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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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FTO genotype is associated with phenotypic variability of body mass index

Jian Yang, +198 more
TL;DR: A meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations indicates that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus.
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Analysis of Human Genetic Linkage, Third Edition. By Jurg Ott. Baltimore and London: The Johns Hopkins University Press, 1999. Pp. 405. $55.00.

TL;DR: The third edition of Dr. Ott's book remains the standard reference on human gene mapping, and the new edition merits a place in the library of anyone with a serious interest in the topic.
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Meta-analysis of genetic association studies and adjustment for multiple testing of correlated SNPs and traits.

TL;DR: Simulation shows that methods for adjusting for multiple correlated tests under several study designs commonly employed in meta‐analyses of genetic association tests accurately control the rate of type I error and achieve improved power over multiple testing adjustments that do not account for correlation between SNPs or traits.
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Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.

TL;DR: In this article, generalized functional linear models (GFLMs) were used to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates.
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Commingling and segregation analyses: comparison of results from a simulation study of a quantitative trait.

TL;DR: Applying both commingling and segregation analyses to samples of simulated pedigree data in which a major locus is segregating in the presence of polygenes and an individual‐specific environmental effect shows limited usefulness as a screening technique for the presence for a single locus.