Clara Albiñana

Feb 26, 2024 | nature.com | Clara Albiñana |Zhihong Zhu |Nis Borbye-Lorenzen |Kristin Skogstrand |Naomi R. Wray |Joana A. Revez | +7 more

Correction to: Nature Communications https://doi.org/10.1038/s41467-023-36392-5, published online 15 February 2023The original version of this article contained an error in the second paragraph of the ‘Assay of DBP concentration’ section of the ‘Methods’, which incorrectly read ‘The lower and upper detection limits for DBP were 2.07 and 79.8 mg/L respectively’. The correct version states ‘2.07 µg/L’ in place of ‘2.07’. This has been corrected in both the PDF and HTML versions of the article.

Nov 12, 2023 | nature.com | Clara Albiñana |Bjarni J Vilhjálmsson |Thomas Werge |David M. Hougaard |Anders D. Børglum |Preben mortensen | +1 more

AbstractIt remains inconclusive whether postpartum depression (PPD) and depression with onset outside the postpartum period (MDD) are genetically distinct disorders. We aimed to investigate whether polygenic risk scores (PGSs) for major mental disorders differ between PPD cases and MDD cases in a nested case-control study of 50,057 women born from 1981 to 1997 in the iPSYCH2015 sample in Demark.

Nov 8, 2023 | cell.com | Florian Privé |Clara Albiñana |Grenoble INP

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Oct 5, 2023 | nature.com | Robin Beaumont |Christopher Flatley |Marc Vaudel |JING CHEN |Gunn-Helen Moen |Line Skotte | +52 more

AbstractA well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n = 65,405), maternal (n = 61,228) and paternal (n = 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1.

Aug 5, 2023 | nature.com | Clara Albiñana |Hugues Aschard |Cynthia Bulik |Jakob Grove |David M. Hougaard |Thomas Werge | +5 more

AbstractThe predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones.