Softwares and computational codes developed by our group
- iQTL-F2: R code for mapping imprinted quantitative trait loci (iQTL) in F2 experimental cross.
[R Code for genomewide scan]
[R Code for imprinting test].
Reference: Cui, Y.H., Q. Lu, J.M. Cheverud, R.C. Littell and R.L. Wu. (2006) Model for mapping imprinted quantitative trait loci in an inbred F2 design. Genomics 87: 543-551 [PDF]
- R-GC-test: R code for testing genetic conflicts (GC) that increase disease risk in human pregnancy with penalized mixture logistic regression.
[R Code for testing genetic conflicts]
Reference: Li, S.Y., Q. Lu, W. Fu, R. Romero and Y.H. Cui. (2008) A regularized regression approach for dissecting genetic conflicts that increase disease risk in pregnancy. Statistical Applications in Genetics and Molecular Biology [PDF]
- QTL-GPR: Matlab code for QTL mapping with count trait.
Reference: Cui, Y.H., D.-Y. Kim, and J. Zhu. (2006) On the generalized Poisson regression mixture model for mapping quantitative trait loci with count data. Genetics 174(4): 2159–2172. [PDF]
- QTL-ZIGP: R code for QTL mapping with Zero Inflated Generalized Poisson regression model.
Reference: Cui, Y.H. and W.Z. Yang. (2009) Zero-inflated generalized poisson regression mixture model for mapping quantitative trait loci underlying count trait with many zeros. Journal of Theoretical Biology 256: 276-285. [PDF];
- iQTL-VC: R code for iQTL mapping with Variance Component model in experimental cross.
Reference: Li, G.X. and Y.H. Cui. (2010) A general statistical framework for dissecting parent-of-origin effects underlying triploid endosperm traits in flowering plants. Annals of Applied Statistics 4(3): 1214-1233. [PDF]|[Link to the Journal].
- HAPAL: R code for mapping HAPlotype-haplotype interactions with Adaptive Lasso.
Reference: Li, M., R. Romero, W.J. Fu and Y.H. Cui. (2010) Mapping haplotype-haplotype interactions with adaptive LASSO. BMC Genetics 11:79 [ PubMed]
- VCGE: R code for gene-environment interaction with varying coefficient model.
Reference:Ma, S.J., L.J. Yang, R. Romero, and Y.H. Cui. (2011) Varying coefficient model for gene-environment interaction: a non-linear look. Bioinformatics 27(15): 2119-2126 [PDF]
- SPA3G: R package for Gene-centric Gene-Gene Interaction.
Reference: Li SY and YH Cui. (2012) Gene-centric gene-gene interaction: a model-based kernel machine method. Annals of Applied Statistics 6(3): 1134-1161. [PDF]
- diSNPselection: Matlab code for disease informative SNP (diSNP) selection for gene-based association tests.
Reference: Wu, C and Y. Cui. (2013) Boosting signals in gene-based association studies via efficient SNP selection. Briefings in Bioinformatics (in press)
- GxE-selection: R code for variable selection for gene-environment interactions.
Reference: Wu, C, P-S. Zhong and Y. Cui. High-dimensional variable selection for gene-environment interactions. [preprint]
- GC-GWAS: Matlab code for gene-centric genomewide association studies via entropy (to be released soon).
Reference: Cui, Y.H., G.L. Kang, K.L. Sun, R. Romero, M. Qian, and W.J. Fu. (2008) Gene-centric genomewide association study via entropy. Genetics 179: 637-650 [PubMed].
Last Modified July, 2010