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- Sun, Jiangming, et al.
(author)
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Genetic Susceptibility to Mood Disorders and Risk of Stroke : A Polygenic Risk Score and Mendelian Randomization Study
- 2023
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In: Stroke. - 1524-4628. ; 54:5, s. 1340-1346
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Journal article (peer-reviewed)abstract
- BACKGROUND: Mood disorders and strokes are often comorbid, and their health toll worldwide is huge. This study characterizes prognostic and causal roles of mood disorders in stroke.METHODS: We tested if genetic susceptibilities for mood disorders were associated with all strokes, ischemic strokes in the Malmö Diet and Cancer cohort (24 631 individuals with a median follow-up of 21.3 (interquartile range: 16.6-23.2) years. We further examined the causal effects for mood disorders on all strokes and ischemic strokes using summary statistics from large genome-wide association studies of mood disorders (up to 609 424 individuals, Psychiatric Genomics Consortium), all strokes and ischemic strokes (up to 446 696 individuals, MEGASTROKE Consortium).RESULTS: Among 24 366 stroke-free participants at baseline, 2632 individuals developed strokes, 2172 of them ischemic, during follow-up. After properly adjusting for well-known risk factors, participants in the highest quintile of polygenic risk scores for mood disorders had 1.45× (95% CI, 1.21-1.74) higher risk of strokes and 1.44× (95% CI, 1.18-1.76) higher risk of ischemic strokes compared with the lowest quintile in women. Mendelian randomization analyses suggested that mood disorders had a causal effect on strokes (odds ratio, 1.07 [95% CI, 1.03-1.11]) and ischemic strokes (odds ratio, 1.09 [95% CI, 1.04-1.13]).CONCLUSIONS: Our results suggest a causal role of mood disorders in the risk of stroke. High-risk women could be identified early in life using polygenic risk scores to ultimately prevent mood disorders and strokes.
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| 2. |
- Sun, Jiangming, et al.
(author)
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Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction
- 2021
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12, s. 5276-5276
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Journal article (peer-reviewed)abstract
- A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual's disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.
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