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Xihong Lin
Researcher at Harvard University
Publications - 389
Citations - 32083
Xihong Lin is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Genome-wide association study. The author has an hindex of 76, co-authored 361 publications receiving 26162 citations. Previous affiliations of Xihong Lin include Texas A&M University & University of Washington.
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Journal ArticleDOI
Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVA
Jun Chen,Ehsan Behnam,Jinyan Huang,Miriam F. Moffatt,Daniel J. Schaid,Liming Liang,Xihong Lin +6 more
TL;DR: SmartSVA corrects the limitation of traditional SVA under highly confounded scenarios by imposing an explicit convergence criterion and improves the computational efficiency for large datasets and can be applied to other genomic studies to capture unknown sources of variability.
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Arsenic reduction in drinking water and improvement in skin lesions: a follow-up study in Bangladesh.
Wei Jie Seow,Wen Chi Pan,Molly L. Kile,Andrea A. Baccarelli,Quazi Quamruzzaman,Mahmuder Rahman,Golam Mahiuddin,Golam Mostofa,Xihong Lin,David C. Christiani +9 more
TL;DR: Reducing arsenic exposure increased the odds that an individual with skin lesions would recover or show less severe lesions within 10 years, and must remain a public health priority in Bangladesh and in other regions affected by arsenic-contaminated water.
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Epigenome-wide DNA methylation changes with development of arsenic-induced skin lesions in Bangladesh: a case-control follow-up study
Wei Jie Seow,Molly L. Kile,Andrea A. Baccarelli,Wen Chi Pan,Hyang-Min Byun,Golam Mostofa,Quazi Quamruzzaman,Mahmuder Rahman,Xihong Lin,David C. Christiani +9 more
TL;DR: DNA methylation changes with the development of arsenic‐induced skin lesions over time was examined but nothing was statistically significant given the small sample size of this exploratory study and the high dimensionality of data.
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Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies.
TL;DR: It is found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of Genome‐Wide SNP data and is implemented in open‐source R software for public use.
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Mixtures of varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout
TL;DR: A mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric, that is used to analyze data from a recent AIDS clinical trial is described.