M
Michael A. Province
Researcher at Washington University in St. Louis
Publications - 409
Citations - 40871
Michael A. Province is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 79, co-authored 396 publications receiving 37334 citations. Previous affiliations of Michael A. Province include Jewish Hospital & Harvard University.
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Journal ArticleDOI
Adult height and prevalence of coronary artery calcium: the National Heart, Lung, and Blood Institute Family Heart Study.
Michael D. Miedema,Andrew B. Petrone,Donna K. Arnett,John A. Dodson,J. Jeffrey Carr,James S. Pankow,Steven C. Hunt,Michael A. Province,Aldi T. Kraja,J. Michael Gaziano,Luc Djoussé +10 more
TL;DR: In this paper, the authors examined the relationship between adult height and the prevalence of coronary artery calcium (CAC), a direct measure of subclinical atherosclerosis and surrogate marker of coronary heart disease.
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Adjusting family relatedness in data-driven burden test of rare variants.
TL;DR: A generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data‐driven burden tests, and allow adaptive and efficient permutation test in family data are developed.
Journal Article
The role of path analysis in coronary heart disease research.
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Inheritance pattern of Beckwith-Wiedemann syndrome is heterogeneous in 291 families with an affected proband.
TL;DR: Familial BWS does not appear to be consistent with autosomal dominant transmission, and is likely a complex mixture of different inheritance patterns, but the presence of families in the cohort consistent with dominant and sex‐dependent inheritance suggest familial BWS may be a heterogeneous group comprised of different Inheritance patterns.
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Tree-Based Models for Fiting Stratified Linear Regression Models
TL;DR: This paper generalizes the methods developed in Shannon, Province, and Rao (2001) to use recursive partitioning to identify subsets of the aggregate data within each of which simple linear regression models give better fit.