Interrogating Associations between Polygenic Liabilities and Electroconvulsive Therapy Effectiveness

Loef, Dore; Luykx, Jurjen J.; Lin, Bochao; Diermen, Linda van; Nuninga, Jasper O.; Exel, Eric van; Oudega, Mardien L.; Rhebergen, Didi; Schouws, Sigfried; Eijndhoven, Philip van; Verwijk, Esmée; Schrijvers, Didier; Birkenhäger, Tom K.; Ryan, Karen M.; Arts, Baer; Bronswijk, Suzanne van; Kenis, Günter; Schurgers, Geert; Baune, Bernhard T.; Arns, Martijn; Dellen, Edwin van; Somers, Metten; Sommer, Iris; Boks, Marco P.; Gülöksüz, Sinan; McLoughlin, Declan M.; Rutten, Bart P. F.; Dols, Annemiek

doi:10.1016/j.biopsych.2022.02.157

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Targeting treatments to those most likely to benefit would be advantageous [ 95 ], particularly for treatments that are costly. While high profile studies have been performed in cardiovascular disease, PGS have been shown to have potential to predict treatment responses to other conditions, including migraine [ 96 ], type 2 diabetes [ 97 ] and psychiatric disorders [ 98 ] like schizophrenia [ 99 ], and depression [ 100 ]. Overall, PGS could prove useful for designing more efficient trials as well as for identifying those most likely to benefit from specific treatments.…”

Section: Potential Benefits Of Polygenic Scoresmentioning

confidence: 99%

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Xiang,

Kelemen,

et al. 2024

Genome Med

View full text Add to dashboard Cite

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals’ genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

show abstract