Joint Association Modeling

Joint Association Modeling is a statistical framework that integrates multiple omics data types to simultaneously assess their associations with a phenotype or outcome, distinguished from sequential or univariate approaches by its capacity to model inter-omics interactions and dependencies within a unified multivariate system.

143

Publications

11K

Citations

775

Authors

430

Institutions

Publications per year

2017–2026

51

Variants

Joint Association Analysis

Authors

775

  1. QL
    Qing Lu

    Michigan State University, United States

    4 publications · 64 citations

    4

    h-index

  2. LQ
    Lu Qi

    Harvard University, United States

    3 publications · 71 citations

    3

    h-index

  3. NM
    Nan M. Laird

    Harvard University, United States

    3 publications · 131 citations

    3

    h-index

  4. RA
    Rebecca A. Betensky

    Harvard University, United States

    3 publications · 91 citations

    3

    h-index

  5. ES
    Emil Sundstrup

    National Research Centre for the Working Environment, Denmark

    3 publications · 105 citations

    3

    h-index

1–5 of 775

Venues

  1. Genetic Epidemiology

    9 publications · 268 citations

    9

    h-index

  2. Biometrics

    7 publications · 247 citations

    7

    h-index

  3. American Journal of Epidemiology

    AJE · 5 publications · 276 citations

    5

    h-index

  4. 5

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 3 publications · 79 citations

    3

    h-index

1–5