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17 results on '"Guan, Wyliena"'

2. MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric

Author

Sun, Quan, primary, Yang, Yingxi, additional, Rosen, Jonathan D., additional, Chen, Jiawen, additional, Li, Xihao, additional, Guan, Wyliena, additional, Jiang, Min-Zhi, additional, Wen, Jia, additional, Pace, Rhonda G., additional, Blackman, Scott M., additional, Bamshad, Michael J., additional, Gibson, Ronald L., additional, Cutting, Garry R., additional, O’Neal, Wanda K., additional, Knowles, Michael R., additional, Kooperberg, Charles, additional, Reiner, Alexander P., additional, Raffield, Laura M., additional, Carson, April P., additional, Rich, Stephen S., additional, Rotter, Jerome I., additional, Loos, Ruth J.F., additional, Kenny, Eimear, additional, Jaeger, Byron C., additional, Min, Yuan-I, additional, Fuchsberger, Christian, additional, and Li, Yun, additional

Published
2024
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3. MagicalRsq-X:A cross-cohort transferable genotype imputation quality metric

Author

Sun, Quan, Yang, Yingxi, Rosen, Jonathan D, Chen, Jiawen, Li, Xihao, Guan, Wyliena, Jiang, Min-Zhi, Wen, Jia, Pace, Rhonda G, Blackman, Scott M, Bamshad, Michael J, Gibson, Ronald L, Cutting, Garry R, O'Neal, Wanda K, Knowles, Michael R, Kooperberg, Charles, Reiner, Alexander P, Raffield, Laura M, Carson, April P., Rich, Stephen S., Rotter, Jerome I., Loos, Ruth J F, Kenny, Eimear, Jaeger, Byron C, Min, Yuan-I, Fuchsberger, Christian, Li, Yun, Sun, Quan, Yang, Yingxi, Rosen, Jonathan D, Chen, Jiawen, Li, Xihao, Guan, Wyliena, Jiang, Min-Zhi, Wen, Jia, Pace, Rhonda G, Blackman, Scott M, Bamshad, Michael J, Gibson, Ronald L, Cutting, Garry R, O'Neal, Wanda K, Knowles, Michael R, Kooperberg, Charles, Reiner, Alexander P, Raffield, Laura M, Carson, April P., Rich, Stephen S., Rotter, Jerome I., Loos, Ruth J F, Kenny, Eimear, Jaeger, Byron C, Min, Yuan-I, Fuchsberger, Christian, and Li, Yun

Abstract

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Published
2024

10. Genetic influences on the shape of brain ventricular and subcortical structures

Author

Zhao, Bingxin, primary, Li, Tengfei, additional, Yang, Xiaochen, additional, Shu, Juan, additional, Wang, Xifeng, additional, Luo, Tianyou, additional, Yang, Yue, additional, Wu, Zhenyi, additional, Fan, Zirui, additional, Jiang, Zhiwen, additional, Chen, Jie, additional, Shan, Yue, additional, Tang, Jiarui, additional, Xiong, Di, additional, Zhu, Ziliang, additional, Gao, Mufeng, additional, Guan, Wyliena, additional, Tomlinson, Chalmer E., additional, Dong, Qunxi, additional, Li, Yun, additional, Stein, Jason L., additional, Wang, Yalin, additional, and Zhu, Hongtu, additional

Published
2022
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11. Genome-wide association study reveals novel genetic loci: a new polygenic risk score for mitral valve prolapse

Author

Roselli, Carolina, primary, Yu, Mengyao, additional, Nauffal, Victor, additional, Georges, Adrien, additional, Yang, Qiong, additional, Love, Katie, additional, Weng, Lu Chen, additional, Delling, Francesca N, additional, Maurya, Svetlana R, additional, Schrölkamp, Maren, additional, Tfelt-Hansen, Jacob, additional, Hagège, Albert, additional, Jeunemaitre, Xavier, additional, Debette, Stéphanie, additional, Amouyel, Philippe, additional, Guan, Wyliena, additional, Muehlschlegel, Jochen D, additional, Body, Simon C, additional, Shah, Svati, additional, Samad, Zainab, additional, Kyryachenko, Sergiy, additional, Haynes, Carol, additional, Rienstra, Michiel, additional, Le Tourneau, Thierry, additional, Probst, Vincent, additional, Roussel, Ronan, additional, Wijdh-Den Hamer, Inez J, additional, Siland, Joylene E, additional, Knowlton, Kirk U, additional, Jacques Schott, Jean, additional, Levine, Robert A, additional, Benjamin, Emelia J, additional, Vasan, Ramachandran S, additional, Horne, Benjamin D, additional, Muhlestein, Joseph B, additional, Benfari, Giovanni, additional, Enriquez-Sarano, Maurice, additional, Natale, Andrea, additional, Mohanty, Sanghamitra, additional, Trivedi, Chintan, additional, Shoemaker, Moore B, additional, Yoneda, Zachary T, additional, Wells, Quinn S, additional, Baker, Michael T, additional, Farber-Eger, Eric, additional, Michelena, Hector I, additional, Lundby, Alicia, additional, Norris, Russell A, additional, Slaugenhaupt, Susan A, additional, Dina, Christian, additional, Lubitz, Steven A, additional, Bouatia-Naji, Nabila, additional, Ellinor, Patrick T, additional, and Milan, David J, additional

Published
2022
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12. Abstract 9757: Frequency of Atrial Fibrillation in a Contemporary Elderly Primary Care Population: A Vital-af Trial Study

Author

Khurshid, Shaan, primary, Ashburner, Jeffrey M, additional, Chang, Yuchiao, additional, Trisini Lipsanopoulos, Ana T, additional, Borowsky, Leila H, additional, Guan, Wyliena, additional, Ellinor, Patrick T, additional, McManus, David D, additional, Atlas, Steven, additional, Singer, Daniel, additional, and Lubitz, Steven A, additional

Published
2021
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13. Screening for Atrial Fibrillation in Older Adults at Primary Care Visits: the VITAL-AF Randomized Controlled Trial

Author

Lubitz, Steven A., primary, Atlas, Steven J., additional, Ashburner, Jeffrey M., additional, Lipsanopoulos, Ana T. Trisini, additional, Borowsky, Leila H., additional, Guan, Wyliena, additional, Khurshid, Shaan, additional, Ellinor, Patrick T., additional, Chang, Yuchiao, additional, McManus, David D., additional, and Singer, Daniel E., additional

Published
2021
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14. Design and rationale of a pragmatic trial integrating routine screening for atrial fibrillation at primary care visits: The VITAL-AF trial

Author

Ashburner, Jeffrey M., primary, Atlas, Steven J., additional, McManus, David D., additional, Chang, Yuchiao, additional, Trisini Lipsanopoulos, Ana T., additional, Borowsky, Leila H., additional, Guan, Wyliena, additional, He, Wei, additional, Ellinor, Patrick T., additional, Singer, Daniel E., additional, and Lubitz, Steven A., additional

Published
2019
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15. Abstract 207: VITAL-AF: A Pragmatic Trial Integrating Routine Screening for Atrial Fibrillation During Primary Care Visits

Author

Ashburner, Jeffrey M, primary, Atlas, Steven J, additional, McManus, David D, additional, Chang, Yuchiao, additional, Trisini Lipsanopoulos, Ana T, additional, Borowsky, Leila H, additional, Guan, Wyliena, additional, He, Wei, additional, Ellinor, Patrick T, additional, Singer, Daniel E, additional, and Lubitz, Steven A, additional

Published
2019
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16. Abstract 13985: Temporal Trends in Implantable Loop Recorder Use After Stroke in a Large Academic Health System

Author

Al-Alusi, Mostafa, Khurshid, Shaan, Guan, Wyliena, Lee, Priscilla R, Lipsanopoulos, Ana, Ellinor, Patrick T, Anderson, Christopher D, and Lubitz, Steven A

Abstract

Introduction:Up to 10-30% of ischemic strokes are cryptogenic. In 2014, a landmark study demonstrated that implantable loop recorders (ILRs) are effective for detecting atrial fibrillation (AF) after cryptogenic stroke. Data on ILR utilization after stroke are not well-described, though some reports suggest that ILR use is infrequent. We examined the frequency of ILR use after incident stroke in an academic health system.Methods:Between 2015 and 2018, incident ischemic strokes were identified in a multi-institutional electronic health record spanning 5 hospitals using billing and neuroimaging study codes (positive predictive value of 91.7%; n = 137 charts reviewed). We assessed temporal trends in ILR use after stroke, while controlling for patient demographics and cardiovascular comorbidities using multivariable logistic regression.Results:Of 6,201 patients with incident ischemic stroke and without prevalent AF, 53.3% were male and the mean age at stroke was 67 ? 16 years. In total, 193 (3.1%) patients underwent ILR placement for AF monitoring within one year of stroke. ILR utilization increased significantly over time (OR 2.27 [95% CI: 1.93, 2.70] per year; p < 0.001), ranging from 0.4% in 2015 to 6.1% in 2018 (Figure). The association between year of stroke and ILR use persisted when controlling for patient demographics and cardiovascular comorbidities. Among the 5559 individuals without AF detected within 30 days, AF was subsequently detected in 23 (12.1%; n = 190) of those who had an ILR placed versus 309 (5.8%; n = 5369) of those who did not (p < 0.001).Conclusions:In a multi-institutional academic health system, adoption of continuous long-term cardiac rhythm monitoring increased over time. However, ILR utilization remains lower than previously reported cryptogenic stroke rates. Future studies are warranted to understand the factors impacting ILR utilization after stroke, and the effectiveness of ILRs for preventing recurrent strokes related to AF.

Published
2019
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17. DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network.

Author

Li Y, Wen J, Li G, Chen J, Sun Q, Liu W, Guan W, Lai B, Szatkiewicz J, He X, and Sullivan P

Abstract

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by ~3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimer's disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.

Published
2023
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