Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample.

Journal: Genes
PMID: 39766785

Abstract

BACKGROUND: The genetic determinants of peripartum depression (PPD) are not fully understood. Using a multi-polygenic score approach, we characterized the relationship between genome-wide information and the history of PPD in patients with mood disorders, with the hypothesis that multiple polygenic risk scores (PRSs) could potentially influence the development of PPD.

Authors

  • Yasmin A Harrington
    Vita-Salute San Raffaele University, 20132 Milan, Italy.
  • Lidia Fortaner-Uyà
    Vita-Salute San Raffaele University, 20132 Milan, Italy.
  • Marco Paolini
    Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Hospital, 20132 Milan, Italy.
  • Sara Poletti
    Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, Psychiatry and Clinical Psychobiology, IRCCS San Raffaele Scientific Institute, Milan, Italy. Electronic address: poletti.sara@hsr.it.
  • Cristina Lorenzi
    Division of Neuroscience, Psychiatry and Clinical Psychobiology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Sara Spadini
    Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Hospital, 20132 Milan, Italy.
  • Elisa M T Melloni
    Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Hospital, 20132 Milan, Italy.
  • Elena Agnoletto
    Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS San Raffaele Hospital, 20132 Milan, Italy.
  • Raffaella Zanardi
    Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy.
  • Cristina Colombo
    Vita-Salute San Raffaele University, Milan, Italy.
  • Francesco Benedetti
    Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, Psychiatry and Clinical Psychobiology, IRCCS San Raffaele Scientific Institute, Milan, Italy.

Keywords

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