Softwares and computational codes
developed by our group
- iQTL-F2: R code for mapping
imprinted quantitative trait loci (iQTL) in F2
[R Code for genomewide scan]
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.
[QTL-ZIGP]; [Sample Data]
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
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. (2014) Boosting signals in gene-based
association studies via efficient SNP selection. Briefings in
Bioinformatics. 15: 279-291.
- GxE-selection: R code for variable
selection for gene-environment interactions.
Reference: Wu, C, P-S. Zhong and Y. Cui. (2015) High-dimensional
variable selection for gene-environment interactions. [preprint]
- caPC: R code for
"Learning directed acyclic graphical structures with genetical
Reference: Gao, B. and Y. Cui. (2015) Learning
directed acyclic graphical structures with genetical genomics data. Bioinformatics 31: 3953-3960.
- PLVMICM: Matlab
and C code for "Partial linear varying multi-index coefficient model
for integrative gene-environment interactions".
Reference: Liu, X., Y. Cui and R. Li. (2016) Partial linear varying
multi-index coefficient model for integrative gene-environment
interactions. Statistica Sinica 26: 1037-1060.
- GA-CKPLS: R code for “Predicting
disease trait with genomic data: A composite kernel approach”.
Reference: Yang, H.T., S. Li, H. Cao,
C. Zhang and Y. Cui. (2016). Predicting disease trait with genomic data: A
composite kernel approach. Briefings in
Bioinformatics (in press)
- VC-sPCR: R code for “A nonlinear model for
gene-based gene-environment interaction”.
Reference: Sa, J., X. Liu, T. He, G.
Liu and Y. Cui. (2016). A nonlinear
model for gene-based gene-environment interaction. 17(6): 882.
- IVGC: R code for
“Integrative analysis of genetical genomics incorporating network
Reference: Gao, B., X. Liu, H. Li and
Y. Cui. (2019). Integrative analysis of genetical genomics incorporating
network structures. Biometrics (in
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Modified May, 2018