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Multidimensional molecular measurements–environment interaction analysis for disease outcomes

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  • Yaqing Xu
  • Mengyun Wu
  • Shuangge Ma

Abstract

Multiple types of molecular (genetic, genomic, epigenetic, etc.) measurements, environmental risk factors, and their interactions have been found to contribute to the outcomes and phenotypes of complex diseases. In each of the previous studies, only the interactions between one type of molecular measurement and environmental risk factors have been analyzed. In recent biomedical studies, multidimensional profiling, in which data from multiple types of molecular measurements are collected from the same subjects, is becoming popular. A myriad of recent studies have shown that collectively analyzing multiple types of molecular measurements is not only biologically sensible but also leads to improved estimation and prediction. In this study, we conduct an M–E interaction analysis, with M standing for multidimensional molecular measurements and E standing for environmental risk factors. This can accommodate multiple types of molecular measurements and sufficiently account for their overlapping as well as independent information. Extensive simulation shows that it outperforms several closely related alternatives. In the analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma and cutaneous melanoma, we make some stable biological findings and achieve stable prediction.

Suggested Citation

  • Yaqing Xu & Mengyun Wu & Shuangge Ma, 2022. "Multidimensional molecular measurements–environment interaction analysis for disease outcomes," Biometrics, The International Biometric Society, vol. 78(4), pages 1542-1554, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1542-1554
    DOI: 10.1111/biom.13526
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    1. Erika S. Helgeson & Qian Liu & Guanhua Chen & Michael R. Kosorok & Eric Bair, 2020. "Biclustering via sparse clustering," Biometrics, The International Biometric Society, vol. 76(1), pages 348-358, March.
    2. Jian Huang & Shuangge Ma & Huiliang Xie, 2006. "Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(3), pages 813-820, September.
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    Cited by:

    1. Kuangnan Fang & Jingmao Li & Qingzhao Zhang & Yaqing Xu & Shuangge Ma, 2023. "Pathological imaging‐assisted cancer gene–environment interaction analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 3883-3894, December.
    2. Liang, Weijuan & Zhang, Qingzhao & Ma, Shuangge, 2024. "Hierarchical false discovery rate control for high-dimensional survival analysis with interactions," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).

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