Bayesian Mendelian Randomization

Bayesian Mendelian Randomization is a statistical framework that integrates Bayesian inference with Mendelian randomization to estimate causal effects in genetic epidemiology by incorporating prior knowledge and uncertainty in genetic instruments, exposure, and outcome relationships.

47

Publications

2.8K

Citations

314

Authors

166

Institutions

Publications per year

2017–2026

42

Authors

314

  1. SB
    Stephen Burgess

    University of Cambridge, United Kingdom

    6 publications · 233 citations

    6

    h-index

  2. JL
    Jin Liu

    Duke-NUS Medical School, Singapore

    3 publications · 401 citations

    3

    h-index

  3. GD
    George Davey Smith

    University of Bristol, United Kingdom

    3 publications · 202 citations

    3

    h-index

  4. JF
    James F. Wilson

    University of Edinburgh, United Kingdom

    2 publications · 134 citations

    2

    h-index

  5. YC
    Yitong Chen

    Zhejiang University, China

    2 publications · 36 citations

    2

    h-index

1–5 of 314

Venues

  1. International Journal of Epidemiology

    3 publications · 161 citations

    3

    h-index

  2. Statistics in Medicine

    3 publications · 91 citations

    3

    h-index

  3. Bioinformatics

    3 publications · 409 citations

    3

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 176 citations

    2

    h-index

  5. The Journal of Clinical Endocrinology & Metabolism

    JCEM · 2 publications · 147 citations

    2

    h-index

1–5