Grant Number:	1R01CA204120-01 Interpret this number
Primary Investigator:	Ma, Shuangge
Organization:	Yale University
Project Title:	Novel Methods for Identifying Genetic Interactions in Cancer Prognosis
Fiscal Year:	2016

 DESCRIPTION (provided by applicant): Project Summary In cancer prognosis, beyond the main effects of environmental/clinical (E) and genetic (G) risk factors, the interactions between G and E factors (G*E interactions) and those between G and G factors (G*G interactions) also play critical roles. The existing findings are insufficient, and there is a strong need for identifing more prognostic interactions. Most of the existing effort has been focused on data collection. In contrast, the development of effective analysis methods has been lagging behind. Compared to data collection, methodological development takes much less resources but is equally critical in making reliable findings. Most of the existing interaction analysis methods share the limitation of lacking robustness properties. In practice, data contamination and model mis-specification are not uncommon and can lead to severely biased model parameter estimation and false marker identification. The development of robust genetic interaction analysis methods is very limited. There are a few methods for case-control data, but they are not applicable to prognosis data. For prognosis data and interaction analysis, there is some very recent progress in quantile regression and rank-based methods, but the development has been limited and unsystematic. Last but not least, the existing robust methods have the common drawback of adopting ineffective marker selection techniques. Our group has been at the frontier of developing robust interaction analysis methods. Our statistical investigations and simulations have provided convincing evidences that the robust methods using the penalization technique outperform alternatives with significantly more accurate marker identification and model parameter estimation. In data analysis, important interactions missed by the existing analyses have been identified for multiple cancer types. However, we have also found that the scope of the existing studies needs to be significantly expanded in terms of both methodological development and data analysis. This project has been motivated by the importance of interactions in cancer prognosis and limitations of the existing studies. Our objectives are as follows. (Aim 1) Develop novel marginal analysis methods that are robust to data contamination and model mis-specification for identifying important interactions. (Aim 2) Develop novel joint analysis methods that are robust to data contamination and model mis-specification for identifying important interactions. (Aim 3) Develop tailored inference approaches to draw more definitive conclusions on the identified interactions. (Aim 4) Develop public R software and a dynamic project website. Identify prognostic interactions for multiple cancers. For the identified interactions, we will conduct extensive bioinformatic and statistical analysis, evaluations, and comparisons. With our unique expertise, extensive experiences, and promising preliminary studies, this project has a high likelihood of success.




The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.
Authors: Liu Y. , Ren J. , Ma S. , Wu C. .
Source: Statistics In Medicine, 2024-09-11 00:00:00.0; , .
EPub date: 2024-09-11 00:00:00.0.
PMID: 39260448
Related Citations

Estimation of multiple networks with common structures in heterogeneous subgroups.
Authors: Qin X. , Hu J. , Ma S. , Wu M. .
Source: Journal Of Multivariate Analysis, 2024 Jul; 202, .
EPub date: 2024-02-13 00:00:00.0.
PMID: 38433779
Related Citations

Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.
Authors: Liang W. , Zhang Q. , Ma S. .
Source: Computational Statistics & Data Analysis, 2024 Apr; 192, .
EPub date: 2023-12-05 00:00:00.0.
PMID: 38098875
Related Citations

Information-incorporated sparse hierarchical cancer heterogeneity analysis.
Authors: Han W. , Zhang S. , Ma S. , Ren M. .
Source: Statistics In Medicine, 2024-03-30 00:00:00.0; , .
EPub date: 2024-03-30 00:00:00.0.
PMID: 38553996
Related Citations

Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering.
Authors: Sun X. , Zhang S. , Ma S. .
Source: Entropy (basel, Switzerland), 2024-03-30 00:00:00.0; 26(4), .
EPub date: 2024-03-30 00:00:00.0.
PMID: 38667864
Related Citations

FunctanSNP: an R package for functional analysis of dense SNP data (with interactions).
Authors: Ren R. , Fang K. , Zhang Q. , Ma S. .
Source: Bioinformatics (oxford, England), 2023-12-01 00:00:00.0; 39(12), .
PMID: 38060266
Related Citations

The Bayesian Regularized Quantile Varying Coefficient Model.
Authors: Zhou F. , Ren J. , Ma S. , Wu C. .
Source: Computational Statistics & Data Analysis, 2023 Nov; 187, .
EPub date: 2023-06-23 00:00:00.0.
PMID: 38746689
Related Citations

Aligned deep neural network for integrative analysis with high-dimensional input.
Authors: Zhang S. , Zhang S. , Yi H. , Ma S. .
Source: Journal Of Biomedical Informatics, 2023 Aug; 144, p. 104434.
EPub date: 2023-06-28 00:00:00.0.
PMID: 37391115
Related Citations

Pathological imaging-assisted cancer gene-environment interaction analysis.
Authors: Fang K. , Li J. , Zhang Q. , Xu Y. , Ma S. .
Source: Biometrics, 2023-05-03 00:00:00.0; , .
EPub date: 2023-05-03 00:00:00.0.
PMID: 37132273
Related Citations

Bi-level structured functional analysis for genome-wide association studies.
Authors: Wu M. , Wang F. , Ge Y. , Ma S. , Li Y. .
Source: Biometrics, 2023-04-26 00:00:00.0; , .
EPub date: 2023-04-26 00:00:00.0.
PMID: 37098961
Related Citations

Bayesian finite mixture of regression analysis for cancer based on histopathological imaging-environment interactions.
Authors: Im Y. , Huang Y. , Tan A. , Ma S. .
Source: Biostatistics (oxford, England), 2023-04-14 00:00:00.0; 24(2), p. 425-442.
PMID: 37057611
Related Citations

Gene-environment interaction analysis via deep learning.
Authors: Wu S. , Xu Y. , Zhang Q. , Ma S. .
Source: Genetic Epidemiology, 2023 Apr; 47(3), p. 261-286.
EPub date: 2023-02-19 00:00:00.0.
PMID: 36807383
Related Citations

Unified model-free interaction screening via CV-entropy filter.
Authors: Xiong W. , Chen Y. , Ma S. .
Source: Computational Statistics & Data Analysis, 2023 Apr; 180, .
EPub date: 2022-12-28 00:00:00.0.
PMID: 36910335
Related Citations

HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.
Authors: Zhong T. , Zhang Q. , Huang J. , Wu M. , Ma S. .
Source: Statistica Sinica, 2023 Apr; 33(2), p. 729-758.
PMID: 38037567
Related Citations

Spatio-temporally smoothed deep survival neural network.
Authors: Li Y. , Liang D. , Ma S. , Ma C. .
Source: Journal Of Biomedical Informatics, 2023 Jan; 137, p. 104255.
EPub date: 2022-12-01 00:00:00.0.
PMID: 36462600
Related Citations

A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data.
Authors: Xiao Z. , Xingjie S. , Yiming L. , Xu L. , Ma S. .
Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2023; 32(3), p. 873-883.
EPub date: 2023-02-06 00:00:00.0.
PMID: 38009111
Related Citations

Two-level Bayesian interaction analysis for survival data incorporating pathway information.
Authors: Qin X. , Ma S. , Wu M. .
Source: Biometrics, 2022-12-16 00:00:00.0; , .
EPub date: 2022-12-16 00:00:00.0.
PMID: 36524727
Related Citations

A tree-based gene-environment interaction analysis with rare features.
Authors: Liu M. , Zhang Q. , Ma S. .
Source: Statistical Analysis And Data Mining, 2022 Oct; 15(5), p. 648-674.
EPub date: 2022-03-01 00:00:00.0.
PMID: 38046814
Related Citations

Sparse group variable selection for gene-environment interactions in the longitudinal study.
Authors: Zhou F. , Lu X. , Ren J. , Fan K. , Ma S. , Wu C. .
Source: Genetic Epidemiology, 2022-06-29 00:00:00.0; , .
EPub date: 2022-06-29 00:00:00.0.
PMID: 35766061
Related Citations

Network-based cancer heterogeneity analysis incorporating multi-view of prior information.
Authors: Li Y. , Xu S. , Ma S. , Wu M. .
Source: Bioinformatics (oxford, England), 2022-05-13 00:00:00.0; 38(10), p. 2855-2862.
PMID: 35561185
Related Citations

Network-based cancer heterogeneity analysis incorporating multi-view of prior information.
Authors: Li Y. , Xu S. , Ma S. , Wu M. .
Source: Bioinformatics (oxford, England), 2022-05-13 00:00:00.0; 38(10), p. 2855-2862.
PMID: 35561185
Related Citations

Biclustering analysis of functionals via penalized fusion.
Authors: Fang K. , Chen Y. , Ma S. , Zhang Q. .
Source: Journal Of Multivariate Analysis, 2022 May; 189, .
EPub date: 2021-10-29 00:00:00.0.
PMID: 36817965
Related Citations

GEInfo: an R package for gene-environment interaction analysis incorporating prior information.
Authors: Wang X. , Liu H. , Ma S. .
Source: Bioinformatics (oxford, England), 2022-04-29 00:00:00.0; , .
EPub date: 2022-04-29 00:00:00.0.
PMID: 35485739
Related Citations

iSFun: an R package for integrative dimension reduction analysis.
Authors: Fang K. , Ren R. , Zhang Q. , Ma S. .
Source: Bioinformatics (oxford, England), 2022-04-20 00:00:00.0; , .
EPub date: 2022-04-20 00:00:00.0.
PMID: 35441661
Related Citations

Integrative functional linear model for genome-wide association studies with multiple traits.
Authors: Li Y. , Wang F. , Wu M. , Ma S. .
Source: Biostatistics (oxford, England), 2022-04-13 00:00:00.0; 23(2), p. 574-590.
PMID: 33040145
Related Citations

Integrative functional linear model for genome-wide association studies with multiple traits.
Authors: Li Y. , Wang F. , Wu M. , Ma S. .
Source: Biostatistics (oxford, England), 2022-04-13 00:00:00.0; 23(2), p. 574-590.
PMID: 33040145
Related Citations

Robust Bayesian variable selection for gene-environment interactions.
Authors: Ren J. , Zhou F. , Li X. , Ma S. , Jiang Y. , Wu C. .
Source: Biometrics, 2022-04-08 00:00:00.0; , .
EPub date: 2022-04-08 00:00:00.0.
PMID: 35394058
Related Citations

Robust Bayesian variable selection for gene-environment interactions.
Authors: Ren J. , Zhou F. , Li X. , Ma S. , Jiang Y. , Wu C. .
Source: Biometrics, 2022-04-08 00:00:00.0; , .
EPub date: 2022-04-08 00:00:00.0.
PMID: 35394058
Related Citations

Bayesian hierarchical finite mixture of regression for histopathological imaging-based cancer data analysis.
Authors: Im Y. , Huang Y. , Huang J. , Ma S. .
Source: Statistics In Medicine, 2022-01-13 00:00:00.0; , .
EPub date: 2022-01-13 00:00:00.0.
PMID: 35028949
Related Citations

Gene-environment interaction identification via penalized robust divergence.
Authors: Ren M. , Zhang S. , Ma S. , Zhang Q. .
Source: Biometrical Journal. Biometrische Zeitschrift, 2021-11-01 00:00:00.0; , .
EPub date: 2021-11-01 00:00:00.0.
PMID: 34725857
Related Citations

Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.
Authors: Qin X. , Ma S. , Wu M. .
Source: Statistics In Medicine, 2021-09-20 00:00:00.0; , .
EPub date: 2021-09-20 00:00:00.0.
PMID: 34542187
Related Citations

Hierarchical cancer heterogeneity analysis based on histopathological imaging features.
Authors: Ren M. , Zhang Q. , Zhang S. , Zhong T. , Huang J. , Ma S. .
Source: Biometrics, 2021-08-14 00:00:00.0; , .
EPub date: 2021-08-14 00:00:00.0.
PMID: 34390584
Related Citations

Marginal false discovery rate for a penalized transformation survival model.
Authors: Liang W. , Ma S. , Lin C. .
Source: Computational Statistics & Data Analysis, 2021 Aug; 160, .
EPub date: 2021-04-02 00:00:00.0.
PMID: 34393307
Related Citations

Multidimensional molecular measurements-environment interaction analysis for disease outcomes.
Authors: Xu Y. , Wu M. , Ma S. .
Source: Biometrics, 2021-07-02 00:00:00.0; , .
EPub date: 2021-07-02 00:00:00.0.
PMID: 34213006
Related Citations

GEInter: an R package for robust gene-environment interaction analysis.
Authors: Wu M. , Qin X. , Ma S. .
Source: Bioinformatics (oxford, England), 2021-05-07 00:00:00.0; , .
EPub date: 2021-05-07 00:00:00.0.
PMID: 33961050
Related Citations

Information-incorporated Gaussian graphical model for gene expression data.
Authors: Yi H. , Zhang Q. , Lin C. , Ma S. .
Source: Biometrics, 2021-02-02 00:00:00.0; , .
EPub date: 2021-02-02 00:00:00.0.
PMID: 33527365
Related Citations

Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling.
Authors: Zhang S. , Fan Y. , Zhong T. , Ma S. .
Source: Scientific Reports, 2020-09-14 00:00:00.0; 10(1), p. 15030.
EPub date: 2020-09-14 00:00:00.0.
PMID: 32929170
Related Citations

Histopathological imaging-based cancer heterogeneity analysis via penalized fusion with model averaging.
Authors: He B. , Zhong T. , Huang J. , Liu Y. , Zhang Q. , Ma S. .
Source: Biometrics, 2020-08-21 00:00:00.0; , .
EPub date: 2020-08-21 00:00:00.0.
PMID: 32822084
Related Citations

Tests for regression coefficients in high dimensional partially linear models.
Authors: Liu Y. , Zhang S. , Ma S. , Zhang Q. .
Source: Statistics & Probability Letters, 2020 Aug; 163, .
EPub date: 2020-04-09 00:00:00.0.
PMID: 32431467
Related Citations

An integrative sparse boosting analysis of cancer genomic commonality and difference.
Authors: Sun Y. , Sun Z. , Jiang Y. , Li Y. , Ma S. .
Source: Statistical Methods In Medical Research, 2020 May; 29(5), p. 1325-1337.
EPub date: 2019-07-07 00:00:00.0.
PMID: 31282286
Related Citations

Genetic susceptibility may modify the association between cell phone use and thyroid cancer: A population-based case-control study in Connecticut.
Authors: Luo J. , Li H. , Deziel N.C. , Huang H. , Zhao N. , Ma S. , Ni X. , Udelsman R. , Zhang Y. .
Source: Environmental Research, 2020 Mar; 182, p. 109013.
EPub date: 2019-12-06 00:00:00.0.
PMID: 31918310
Related Citations

Semiparametric Bayesian variable selection for gene-environment interactions.
Authors: Ren J. , Zhou F. , Li X. , Chen Q. , Zhang H. , Ma S. , Jiang Y. , Wu C. .
Source: Statistics In Medicine, 2019-12-21 00:00:00.0; , .
EPub date: 2019-12-21 00:00:00.0.
PMID: 31863500
Related Citations

NCutYX: a package for clustering analysis of multilayer omics data.
Authors: Teran Hidalgo S.J. , Wu M. , Ma S. .
Source: Bioinformatics (oxford, England), 2019-11-15 00:00:00.0; , .
EPub date: 2019-11-15 00:00:00.0.
PMID: 31730176
Related Citations

Identification of gene-environment interactions with marginal penalization.
Authors: Zhang S. , Xue Y. , Zhang Q. , Ma C. , Wu M. , Ma S. .
Source: Genetic Epidemiology, 2019-11-14 00:00:00.0; , .
EPub date: 2019-11-14 00:00:00.0.
PMID: 31724772
Related Citations

Structured gene-environment interaction analysis.
Authors: Wu M. , Zhang Q. , Ma S. .
Source: Biometrics, 2019-08-19 00:00:00.0; , .
EPub date: 2019-08-19 00:00:00.0.
PMID: 31424088
Related Citations

Integrative Analysis of Cancer Omics Data for Prognosis Modeling.
Authors: Wang S. , Wu M. , Ma S. .
Source: Genes, 2019-08-09 00:00:00.0; 10(8), .
EPub date: 2019-08-09 00:00:00.0.
PMID: 31405076
Related Citations

Robust semiparametric gene-environment interaction analysis using sparse boosting.
Authors: Wu M. , Ma S. .
Source: Statistics In Medicine, 2019-07-29 00:00:00.0; , .
EPub date: 2019-07-29 00:00:00.0.
PMID: 31359454
Related Citations

Identifying gene-environment interactions incorporating prior information.
Authors: Wang X. , Xu Y. , Ma S. .
Source: Statistics In Medicine, 2019-04-30 00:00:00.0; 38(9), p. 1620-1633.
EPub date: 2019-01-13 00:00:00.0.
PMID: 30637789
Related Citations

Histopathological Imaging⁻Environment Interactions in Cancer Modeling.
Authors: Xu Y. , Zhong T. , Wu M. , Ma S. .
Source: Cancers, 2019-04-24 00:00:00.0; 11(4), .
EPub date: 2019-04-24 00:00:00.0.
PMID: 31022926
Related Citations

Penalized integrative semiparametric interaction analysis for multiple genetic datasets.
Authors: Li Y. , Li R. , Lin C. , Qin Y. , Ma S. .
Source: Statistics In Medicine, 2019-04-16 00:00:00.0; , .
EPub date: 2019-04-16 00:00:00.0.
PMID: 30993736
Related Citations