First Header Logo Second Header Logo


Ramon Casanova to Genome-Wide Association Study

This is a "connection" page, showing publications Ramon Casanova has written about Genome-Wide Association Study.
Connection Strength

  1. Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep. 2024 May 30; 14(1):12436.
    View in: PubMed
    Score: 0.206
  2. Lutz MW, Casanova R, Saldana S, Kuchibhatla M, Plassman BL, Hayden KM. Analysis of pleiotropic genetic effects on cognitive impairment, systemic inflammation, and plasma lipids in the Health and Retirement Study. Neurobiol Aging. 2019 08; 80:173-186.
    View in: PubMed
    Score: 0.036
  3. Thambisetty M, Casanova R, Varma S, Legido Quigley C. Peril beyond the winner's curse: A small sample size is the bane of biomarker discovery. Alzheimers Dement. 2017 05; 13(5):606-607.
    View in: PubMed
    Score: 0.031
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.