SUPR
Omics data in perinatal mental health
Dnr:

sens2022579

Type:

SNIC SENS

Principal Investigator:

Ulf Elofsson

Affiliation:

Uppsala universitet

Start Date:

2022-10-13

End Date:

2024-11-01

Primary Classification:

30220: Obstetrics, Gynaecology and Reproductive Medicine

Webpage:

Allocation

  • Castor /proj at UPPMAX: 750 GiB
  • Castor /proj/nobackup at UPPMAX: 750 GiB
  • Cygnus /proj/nobackup at UPPMAX: 750 GiB
  • Cygnus /proj at UPPMAX: 750 GiB
  • Bianca at UPPMAX: 2 x 1000 core-h/month

Abstract

One in seven women suffer from depression that onsets either during pregnancy or in the first year after childbirth (PPD, peripartum depression). This has a negative impact not only on the health and wellbeing of the woman and the child, but is also associated with extensive costs for society, especially the health care sector. To decrease both the suffering and cost associated with PPD, effective and timely intervention should be given. To achieve timely treatment, early detection through identification of women at risk for developing PPD is paramount. While screening tools through validated questionnaires are available, there is still limited studies on biological markers to predict PPD, especially at the transcriptome and epigenome levels. Hence, the purpose of our project is to investigate transcriptomic and epigenomic biological differences between women developing PPD and healthy new mothers with the goal of identifying predictive biological markers of PPD. We will look at the gene expression and methylation in the blood of women during early pregnancy, selecting cases (women who develops depression during late pregnancy or postpartum) and controls (women who do not develop depression during late pregnancy or postpartum) from the ‘Biology, Affect, Stress, Imaging, and Cognition in pregnancy and puerperium’ (BASIC) study in Uppsala, Sweden. A range of various possible markers at the transcriptome and epigenetic levels will be determined to identify women who may develop PPD. By identifying biological markers of PPD that can be detected prior to its onset, we can then predict PPD in time for effective treatments.