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Xiang Zhou
Researcher at University of Michigan
Publications - 172
Citations - 20187
Xiang Zhou is an academic researcher from University of Michigan. The author has contributed to research in topics: Medicine & Genome-wide association study. The author has an hindex of 33, co-authored 133 publications receiving 16025 citations. Previous affiliations of Xiang Zhou include Duke University & University of Chicago.
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Detecting Epistasis with the Marginal Epistasis Test in Genetic Mapping Studies of Quantitative Traits
TL;DR: A novel, alternative strategy for mapping epistasis is presented: instead of directly identifying individual pairwise or higher-order interactions, this work focuses on mapping variants that have non-zero marginal epistatic effects — the combined pairwise interaction effects between a given variant and all other variants.
Posted ContentDOI
Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis
TL;DR: This work compared 18 different DR methods on 30 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes and evaluated the performance of different DR Methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix.
Journal ArticleDOI
CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies.
Yi Yang,Yi Yang,Xingjie Shi,Xingjie Shi,Yuling Jiao,Jian Huang,Min Chen,Xiang Zhou,Lei Sun,Xinyi Lin,Xinyi Lin,Can Yang,Jin Liu +12 more
TL;DR: A novel probabilistic model is proposed, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data.
Journal ArticleDOI
Genetic investigation of fibromuscular dysplasia identifies risk loci and shared genetics with common cardiovascular diseases.
Adrien Georges,Min-Lee Yang,Takiy-Eddine Berrandou,Mark K Bakker,Ozan Dikilitas,Soto Romuald Kiando,Lijiang Ma,Benjamin A. Satterfield,Sebanti Sengupta,Mengyao Yu,Jean-François Deleuze,Délia Dupré,Kristina L. Hunker,Sergiy Kyryachenko,Lu Liu,Ines Sayoud-Sadeg,Laurence Amar,Chad M. Brummett,Dawn M. Coleman,Valentina d'Escamard,Peter W. de Leeuw,Natalia Fendrikova-Mahlay,Daniella Kadian-Dodov,Jun Li,Aurélien Lorthioir,Marco Pappaccogli,Marco Pappaccogli,Aleksander Prejbisz,Witold Smigielski,James C. Stanley,Matthew Zawistowski,Xiang Zhou,Sebastian Zöllner,Philippe Amouyel,Marc De Buyzere,Stéphanie Debette,Piotr Dobrowolski,Wojciech Drygas,Heather L. Gornik,Jeffrey W. Olin,Jerzy Piwonski,Ernst Rietzschel,Ynte M. Ruigrok,Miikka Vikkula,Ewa Warchol Celinska,Andrzej Januszewicz,Iftikhar J. Kullo,Michel Azizi,Xavier Jeunemaitre,Alexandre Persu,Alexandre Persu,Jason C. Kovacic,Jason C. Kovacic,Jason C. Kovacic,Santhi K. Ganesh,Nabila Bouatia-Naji +55 more
TL;DR: This paper performed a genome-wide association meta-analysis of Fibromuscular dysplasia to identify genetic loci, some of which are shared with common cardiovascular disease and traits.
Posted Content
Bayesian Approximate Kernel Regression with Variable Selection
TL;DR: A novel framework that provides an effect size analog for each explanatory variable in Bayesian kernel regression models when the kernel is shift-invariant—for example, the Gaussian kernel.