Browsing by Author "Li, Mingyao"

Now showing 1 - 4 of 4
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    Asian Cohort for Alzheimer's Disease (ACAD) pilot study on genetic and non-genetic risk factors for Alzheimer's disease among Asian Americans and Canadians
    (Wiley, 2024) Ho, Pei-Chuan; Yu, Wai Haung; Tee, Boon Lead; Lee, Wan-Ping; Li, Clara; Gu, Yian; Yokoyama, Jennifer S.; Reyes-Dumeyer, Dolly; Choi, Yun-Beom; Yang, Hyun-Sik; Vardarajan, Badri N.; Tzuang, Marian; Lieu, Kevin; Lu, Anna; Faber, Kelley M.; Potter, Zoë D.; Revta, Carolyn; Kirsch, Maureen; McCallum, Jake; Mei, Diana; Booth, Briana; Cantwell, Laura B.; Chen, Fangcong; Chou, Sephera; Clark, Dewi; Deng, Michelle; Hong, Ting Hei; Hwang, Ling-Jen; Jiang, Lilly; Joo, Yoonmee; Kang, Younhee; Kim, Ellen S.; Kim, Hoowon; Kim, Kyungmin; Kuzma, Amanda B.; Lam, Eleanor; Lanata, Serggio C.; Lee, Kunho; Li, Donghe; Li, Mingyao; Li, Xiang; Liu, Chia-Lun; Liu, Collin; Liu, Linghsi; Lupo, Jody-Lynn; Nguyen, Khai; Pfleuger, Shannon E.; Qian, James; Qian, Winnie; Ramirez, Veronica; Russ, Kristen A.; Seo, Eun Hyun; Song, Yeunjoo E.; Tartaglia, Maria Carmela; Tian, Lu; Torres, Mina; Vo, Namkhue; Wong, Ellen C.; Xie, Yuan; Yau, Eugene B.; Yi, Isabelle; Yu, Victoria; Zeng, Xiaoyi; St. George-Hyslop, Peter; Au, Rhoda; Schellenberg, Gerard D.; Dage, Jeffrey L.; Varma, Rohit; Hsiung, Ging-Yuek R.; Rosen, Howard; Henderson, Victor W.; Foroud, Tatiana; Kukull, Walter A.; Peavy, Guerry M.; Lee, Haeok; Feldman, Howard H.; Mayeux, Richard; Chui, Helena; Jun, Gyungah R.; Ta Park, Van M.; Chow, Tiffany W.; Wang, Li-San; Medical and Molecular Genetics, School of Medicine
    Introduction: Clinical research in Alzheimer's disease (AD) lacks cohort diversity despite being a global health crisis. The Asian Cohort for Alzheimer's Disease (ACAD) was formed to address underrepresentation of Asians in research, and limited understanding of how genetics and non-genetic/lifestyle factors impact this multi-ethnic population. Methods: The ACAD started fully recruiting in October 2021 with one central coordination site, eight recruitment sites, and two analysis sites. We developed a comprehensive study protocol for outreach and recruitment, an extensive data collection packet, and a centralized data management system, in English, Chinese, Korean, and Vietnamese. Results: ACAD has recruited 606 participants with an additional 900 expressing interest in enrollment since program inception. Discussion: ACAD's traction indicates the feasibility of recruiting Asians for clinical research to enhance understanding of AD risk factors. ACAD will recruit > 5000 participants to identify genetic and non-genetic/lifestyle AD risk factors, establish blood biomarker levels for AD diagnosis, and facilitate clinical trial readiness. Highlights: The Asian Cohort for Alzheimer's Disease (ACAD) promotes awareness of under-investment in clinical research for Asians. We are recruiting Asian Americans and Canadians for novel insights into Alzheimer's disease. We describe culturally appropriate recruitment strategies and data collection protocol. ACAD addresses challenges of recruitment from heterogeneous Asian subcommunities. We aim to implement a successful recruitment program that enrolls across three Asian subcommunities.
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    Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
    (Elsevier, 2023) Hu, Fengling; Chen, Andrew A.; Horng, Hannah; Bashyam, Vishnu; Davatzikos, Christos; Alexander-Bloch, Aaron; Li, Mingyao; Shou, Haochang; Satterthwaite, Theodore D.; Yu, Meichen; Shinohara, Russell T.; Radiology and Imaging Sciences, School of Medicine
    Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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    Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
    (Springer Nature, 2024) Zhang, Daiwei; Schroeder, Amelia; Yan, Hanying; Yang, Haochen; Hu, Jian; Lee, Michelle Y. Y.; Cho, Kyung S.; Susztak, Katalin; Xu, George X.; Feldman, Michael D.; Lee, Edward B.; Furth, Emma E.; Wang, Linghua; Li, Mingyao; Pathology and Laboratory Medicine, School of Medicine
    Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.
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    Pathologic gene network rewiring implicates PPP1R3A as a central regulator in pressure overload heart failure
    (Springer Nature, 2019-06-24) Cordero, Pablo; Parikh, Victoria N.; Chin, Elizabeth T.; Erbilgin, Ayca; Gloudemans, Michael J.; Shang, Ching; Huang, Yong; Chang, Alex C.; Smith, Kevin S.; Dewey, Frederick; Zaleta, Kathia; Morley, Michael; Brandimarto, Jeff; Glazer, Nicole; Waggott, Daryl; Pavlovic, Aleksandra; Zhao, Mingming; Moravec, Christine S.; Tang, W. H. Wilson; Skreen, Jamie; Malloy, Christine; Hannenhalli, Sridhar; Li, Hongzhe; Ritter, Scott; Li, Mingyao; Bernstein, Daniel; Connolly, Andrew; Hakonarson, Hakon; Lusis, Aldons J.; Margulies, Kenneth B.; Depaoli-Roach, Anna A.; Montgomery, Stephen B.; Wheeler, Matthew T.; Cappola, Thomas; Ashley, Euan A.; Biochemistry and Molecular Biology, School of Medicine
    Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure.