Publications by authors named "S O Dorn"

Ensemble learning has been increasingly popular for boosting the predictive power of polygenic risk scores (PRS), with almost every recent multi-ancestry PRS approach employing ensemble learning as a final step. Existing ensemble approaches rely on individual-level data for model training, which severely limits their real-world applications, especially in non-European populations without sufficient genomic samples. Here, we introduce a statistical framework to construct regularized ensemble PRS, which allows us to combine a large number of candidate PRS models using only summary statistics from genome-wide association studies.

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Shape memory hydrogels constitute a highly attractive materials class that bears enormous potential within a broad range of areas - from engineering to medicine. Within the present contribution we demonstrate that energetic electron crosslinked methylcellulose-only hydrogels exhibit an "inverse" shape memory effect that transforms from a secondary shape to its primary shape upon cooling. The primary shape can conveniently be "programmed" by application of energetic electrons.

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Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and nonrandom participation in parental illness surveys, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that the current AD GWAX provide highly misleading genetic correlations between AD risk and higher education, which subsequently affects a variety of genetic epidemiological applications involving AD and cognition.

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Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification.

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Bone marrow aspirate concentrate (BMAC) and adipose-derived stromal vascular fraction (ADSVF) are the most marketed stem cell therapies to treat a variety of conditions in the general population and elite athletes. Both tissues have been used interchangeably clinically even though their detailed composition, heterogeneity, and mechanisms of action have neither been rigorously inventoried nor compared. This lack of information has prevented investigations into ideal dosages and has facilitated anecdata and misinformation.

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