Xihong Lin's Group @ Harvard School of Public Health

SKAT (SNP Set/Sequence Association Test)

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.

References:

Lee, Seunggeun, et al. (2012). Optimal Unified Approach for Rare-Variant Association Testing with Application to Small-Sample Case-Control Whole-Exome Sequencing Studies . The American Journal of Human Genetics, 91.2, 224-237.
Lee, S., Wu, M.C. and Lin, X. (2012). Optimal tests for rare variant effects in sequencing association studies. Biostatistics, 13.4, 762-775. Supplementary Materials.
Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X (2011) Rare Variant Association Testing for Sequencing Data Using the Sequence Kernel Association Test (SKAT). American Journal of Human Genetics, , 89.1, 82-93.
Wu, M. C., Kraft, P., Epstein, M. P.,Taylor, D., M., Chanock, S. J., Hunter, D., J., and Lin, X. (2010) Powerful SNP Set Analysis for Case-Control GenomeWide Association Studies. American Journal of Human Genetics, , 86, 929-942.

MetaSKAT (Meta-analysis for multiple markers)

MetaSKAT is a R package for multiple marker meta-analysis across studies. It can carry out meta-analysis of SKAT, SKAT-O and burden tests with individual level genotype data or gene level summary statistics.

References:

Lee, S., Teslovich, T.M., Boehnke, M. and Lin, X. (2013) General framework for meta-analysis of rare variants in sequencing association studies, American Journal of Human Genetics, in press.

CEPSKAT (Continuous Extreme Phenotype SKAT)

CEPSKAT extends the SKAT framework to the setting of continuous extreme phenotype samples. You can download the R package for CEPSKAT here. For Windows, download the compiled binary version instead. Consult the help files in the package for instruction and examples of usage.

References:

Barnett, I., Lee, S., Lin, X. (2012) Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies . Genetic Epidemiology . In press.

SMAT (Scaled Multiple-phenotype Association Test)

SMAT is an R package for performing the Scaled Multiple-phenotype Association Test in cohort or case-control designs to assess common effect of a single nucleotide polymorphism (SNP) on multiple (positively correlated) continuous outcomes measuring the same underlying trait.

The current version of the R package is 0.98. Please download the source .tar.gz file or the .zip file for installation. Please download the manual PDF here. Some example files are also available for download.

References:

Schifano, E.D., Li, L., Christiani, D.C., and Lin, X. (2012) Genome-wide Association Analysis for Multiple Continuous Secondary Phenotypes. (in revision)
Roy, J., Lin, X., and Ryan, L. (2003). Scaled Marginal Models For Multiple Continuous Outcomes. Biostatistics, 4, 371-384.

Sparse PCA

R functions for sparse PCA and some examples.

References:

Lee, S., Epstein, M.P., Duncan, R. and Lin, X. (2012) Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies. Genetic Epidemiology , 36.4, 293-302.

Pathway Analysis

sLDA Pathway Test

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

Logistic Kernel Machine

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.

Least Square Kernel Machine

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

References:

Wu, M.,C., Zhang, L., Wang, Z., Christiani, D. C., Lin, Sparse linear discriminant analysis for simultaneous gene set/pathway significance test and gene selection. , Bioinformatics, , 25,1145-1151.
Liu, D., Ghosh, D. and Lin, X. (2008) Estimation and Testing for the Effect of a Genetic Pathway on a Disease Outcome Using Logistic Kernel Machine Regression via Logistic Mixed Models. BMC Bioinformatics, 9, 292.
Liu, D., Lin, X. and Ghosh, D. (2007) Semiparametric Regression of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines and Linear Mixed Models. Biometrics, 63, 1079-1088.

Nonparametric Regression

SAS Macro SPMM

A SAS Macro to fit smoothing splines mixed models, with documentation.

SAS Macro Spline_Mixed

A SAS Macro for calculating a cubic smoothing spline using PROC MIXED.

SAS Macro GAMM1

A SAS Macro to fit generalized additive mixed models using smoothing splines.

References:

Zhang D., Lin X., Raz J., and Sowers M. (1998). Semiparametric stochastic mixed models for longitudinal data, Journal of the American Statistical Association, 93, 710-719.
Lin X. and Zhang D. (1999). Inference in generalized additive mixed models using smoothing splines, Journal of the Royal Statistical Society, Series B, 61, 381-400.
Zhang D., Lin X. and Sowers M. (2000). Periodic semiparametric regression for longitudinal hormone data from multiple menstrual cycles. Biometrics, , 56, 31-39.