Browsing by Author "Wei, Yu-Chung"

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A simple Bayesian mixture model with a hybrid procedure for genome-wide association studies
(2010-08-01)
Wei, Yu-Chung
;
Wen, Shu-Hui
;
Chen, Pei-Chun
;
Wang, Chih-Hao
;
Hsiao, Chuhsing K.
Genome-wide association studies often face the undesirable result of either failing to detect any influential markers at all because of a stringent level for testing error corrections or encountering difficulty in quantifying the importance of markers by their P-values. Advocates of estimation procedures prefer to estimate the proportion of association rather than test significance to avoid overinterpretation. Here, we adopt a Bayesian hierarchical mixture model to estimate directly the proportion of influential markers, and then proceed to a selection procedure based on the Bayes factor (BF). This mixture model is able to accommodate different sources of dependence in the data through only a few parameters. Specifically, we focus on a standardized risk measure of unit variance so that fewer parameters are involved in inference. The expected value of this measure follows a mixture distribution with a mixing probability of association, and it is robust to minor allele frequencies. Furthermore, to select promising markers, we use the magnitude of the BF to represent the strength of evidence in support of the association between markers and disease. We demonstrate this procedure both with simulations and with SNP data from studies on rheumatoid arthritis, coronary artery disease, and Crohn's disease obtained from the Wellcome Trust Case-Control Consortium. This Bayesian procedure outperforms other existing methods in terms of accuracy, power, and computational efficiency. The R code that implements this method is available at http://homepage.ntu.edu.tw/similar to ckhsiao/Bmix/Bmix.htm. European Journal of Human Genetics (2010) 18, 942-947; doi:10.1038/ejhg.2010.51; published online 21 April 2010
CONY: A Bayesian procedure for detecting copy number variations from sequencing read depths
(2020-06-26)
Wei, Yu-Chung
;
Huang, Guan-Hua
;
統計學研究所
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Institute of Statistics
Copy number variations (CNVs) are genomic structural mutations consisting of abnormal numbers of fragment copies. Next-generation sequencing of read-depth signals mirrors these variants. Some tools used to predict CNVs by depth have been published, but most of these tools can be applied to only a specific data type due to modeling limitations. We develop a tool for copy number variation detection by a Bayesian procedure, i.e., CONY, that adopts a Bayesian hierarchical model and an efficient reversible-jump Markov chain Monte Carlo inference algorithm for whole genome sequencing of read-depth data. CONY can be applied not only to individual samples for estimating the absolute number of copies but also to case-control pairs for detecting patient-specific variations. We evaluate the performance of CONY and compare CONY with competing approaches through simulations and by using experimental data from the 1000 Genomes Project. CONY outperforms the other methods in terms of accuracy in both single-sample and paired-samples analyses. In addition, CONY performs well regardless of whether the data coverage is high or low. CONY is useful for detecting both absolute and relative CNVs from read-depth data sequences. The package is available at https://github.com/weiyuchung/CONY.
Trajectories of caregiver depressive symptoms while providing end-of-life care
(2013-12-01)
Tang, Siew Tzuh
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Huang, Guan-Hua
;
Wei, Yu-Chung
;
Chang, Wen-Cheng
;
Chen, Jen-Shi
ObjectiveThe course of caregivers' depressive symptoms may not be homogenous. This study identified trajectories of depressive symptoms among caregivers providing end-of-life care to cancer patients and profiled the unique characteristics of caregivers within each trajectory. MethodsTrajectories of depressive symptoms were explored in 447 caregivers who completed the Center for Epidemiological Studies Depression Scale over four periods close to the patient's death (1-30, 31-90, 91-180, and >180days). Distinct trajectories were identified by latent class analysis. ResultsFour trajectories were identified as endurance, resilience, moderately symptomatic, and chronically distressed and contained 32.0%, 11.4%, 36.9%, and 19.7% of the sample, respectively. Caregivers in the endurance trajectory were relatively well-adjusted individuals with less education, adequate financial support, and ample psychological resources but provided care to older patients with greater symptom distress. They perceived less subjective caregiving burden than caregivers with moderate or chronic depressive symptoms. Caregivers in the resilience trajectory were in a more vulnerable position than those in other trajectories when they first transitioned into the caregiving role because they were more likely to be the patient's spouse, have greater educational attainment and insufficient finances, provide higher intensity assistance to a younger relative, and have weaker psychological resources. However, they were older, reported greater confidence in caregiving, and perceived less caregiving burden than caregivers in other trajectories. The moderately symptomatic and chronically distressed trajectories were differentiated only by the strength of psychological resources. ConclusionsCaregivers of terminally ill cancer patients follow distinct depressive-symptom trajectories while providing end-of-life care. Copyright (c) 2013 John Wiley & Sons, Ltd.