Biology subjects

Dorn, S.

Publications and source records attributed to Dorn, S..

5 recordsLinked to original sources

bioRxiv2025

Genetic basis of partner choice

Previous genetic studies of human assortative mating have primarily focused on searching for its genomic footprint but have revealed limited insights into its biological and social mechanisms. Combining insights from the economics of the marriage market with advanced tools in statistical genetics, we perform the first genome-wide association study (GWAS) on a latent index for partner choice. Using 206,617 individuals from four global cohorts, we uncover phenotypic characteristics and social processes underlying assortative mating. We identify a broadly robust genetic component of the partner choice index between sexes and several countries and identify its genetic correlates. We also provide solutions to reduce assortative mating-driven biases in genetic studies of complex traits by conditioning GWAS summary statistics on the genetic associations with the latent partner choice index.

genetics↗

bioRxiv2024

One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics

Ensemble learning has become a cornerstone for improving the predictive accuracy of polygenic risk scores (PRS), and nearly all recent multi-ancestry PRS methods incorporate ensemble learning as a final step. However, existing ensemble approaches require individual-level genotype data for model training, which limits their real-world applications, especially in non-European populations without sufficient genomic samples. Here, we introduce a statistical framework for constructing regularized ensemble PRS that integrates a large number of candidate PRS models using only genome-wide association study summary statistics. Through extensive analyses across multiple traits and populations, we demonstrate that our method consistently outperforms state-of-the-art PRS approaches within and across ancestries. This framework presents "one score to rule them all" for its capability to enable seamless integration of newly developed PRS models with existing ones, providing a scalable and general solution for future PRS development and application.

genetics↗

bioRxiv2024

Prostate-specific membrane receptor PPAP facilitates E. coli invasion of luminal prostate cells via FimH binding

Bacterial prostatitis caused by uropathogenic Escherichia coli (UPEC) strains is a highly prevalent and recurrent infection responsible for significant morbidity in men. However, the molecular pathogenesis of prostatitis remains poorly understood, partly due to the lack of comprehensive in-vitro models. In this study, we introduce a murine prostate organoid model that replicates the cellular heterogeneity of the prostate epithelium with a cell composition and transcriptional signature comparable to the native prostate tissue. Using this model, we uncovered that UPEC preferentially attaches to, invades, and replicates within luminal prostate cells. This selective interaction is mediated by the binding of the bacterial adhesin FimH to the prostate- specific membrane protein PAPP, which is exclusively expressed on luminal prostate cells. Altogether, we identified a new mechanism by which UPEC infects the prostate epithelium, highlighting FimHs adaptability in engaging host receptors and its potential for targeted therapeutic strategies.

molecular biology↗

bioRxiv2024

Controlling for polygenic genetic confounding in epidemiologic association studies

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. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

genetics↗

bioRxiv2023

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease

Almost every recent Alzheimers 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 non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

genetics↗