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Lin Laboratory

Location: Department of Biostatistics, Harvard School of Public Health, Building II, 4th Floor, 655 Huntington Ave, Boston, MA 02115


Xihong Lin is Professor of Biostatistics and Co-ordinating Director of Program in Quantitative Genomics of Harvard School of Public Health. My group's major research interests lie in development and application of statistical and computational methods for analysis of high-dimensional genomic and 'omics data in population and clinical sciences, and for analysis of correlatd data, such as longitudinal, clustered and spatial data.

We are interested in statistical genetics and genomics, genetic and epigenetic epidemiology, genes and environment and medical genomics. Current research projects include genome-wide association studies, next generation sequencing studies, gene-environment interactions, and genome-wide DNA methylation studies, pathway analysis and network analysis, proteomics.





  • Least Square Kernel Machine ( Algorithmic software component )

    A SAS Macro for doing semiparametric regression of multi-dimensional genetic pathway data, using least squares kernel machines and linear mixed models.

  • Logistic Kernel Machine ( Algorithmic software component )

    A SAS Macro for estimating and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models.

  • SKAT ( Algorithmic software component )

    "SKAT is a R package for performing

    (1) association tests between a set of common and rare SNPs and continuous and dichotomous (case-control) phenotypes using kernel machine methods for data from GWAS and genome-wide sequencing association studies

    (2) sample size and power calculatons for sequencing association studies."

  • sLDA Pathway Test ( Algorithmic software component )

    An R function for testing for differential expression of a gene set/pathway based on the sparse linear discriminant analysis approach.

  • Sparse PCA ( Algorithmic software component )

    R functions for sparse principal component analysis.

  • SPMM ( Algorithmic software component )

    "This macro fits the following model to longitudinal Gaussian data:

    Yij = Xij*beta + f(tij) + Zij*bi + Ui(tij) + esp(ij),

    where beta is parametric fixed effects, f(.) is a smooth function, bi is random effects, Ui(.) is a Gaussian process, esp(ij) is the measurement error. Maximum penalized likelihood was used to estimate the beta and f(.), while smoohting parameter and the parameters in the variance matrix are estimated by REML method, which treats f(.) as an integrated Wiener process."

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Last updated: 2012-11-08T11:14:55.094-06:00

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The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016