Bayesian Fine-mapping

Bayesian Fine-Mapping is a statistical framework for genetic fine-mapping that uses Bayesian inference to compute posterior probabilities of causality for genetic variants, distinguishing itself from frequentist approaches by incorporating prior knowledge and quantifying uncertainty in a probabilistic manner.

28

Publications

1.8K

Citations

240

Authors

113

Institutions

Publications per year

2017–2026

19

Authors

240

  1. CW
    Chris Wallace

    University of Cambridge, United Kingdom

    4 publications · 166 citations

    4

    h-index

  2. AP
    Andrew P. Morris

    Centre for Human Genetics, United Kingdom

    3 publications · 154 citations

    3

    h-index

  3. JC
    John C. Whittaker

    London School of Hygiene & Tropical Medicine, United Kingdom

    3 publications · 130 citations

    3

    h-index

  4. SS
    Stephen Sawcer

    University of Cambridge, United Kingdom

    2 publications · 86 citations

    2

    h-index

  5. DJ
    David J. Balding

    University College London, United Kingdom

    2 publications · 119 citations

    2

    h-index

1–5 of 240

Venues

  1. Genetics

    4 publications · 375 citations

    4

    h-index

  2. Communications Biology

    2 publications · 209 citations

    2

    h-index

  3. Genetic Epidemiology

    2 publications · 53 citations

    2

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 2 publications · 67 citations

    2

    h-index

  5. The American Journal of Human Genetics

    AJHG · 2 publications · 127 citations

    2

    h-index

1–5