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Sökning: WFRF:(Linner L)

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  • Frazier-Wood, Alexis C., et al. (författare)
  • Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses
  • 2016
  • Ingår i: Nature Genetics. - : Nature Research (part of Springer Nature). - 1061-4036 .- 1546-1718. ; 48, s. 624-
  • Tidskriftsartikel (refereegranskat)abstract
    • Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (vertical bar(p) over cap vertical bar approximate to 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.
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  • Becker, Joel, et al. (författare)
  • Resource profile and user guide of the Polygenic Index Repository
  • 2021
  • Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
  • Tidskriftsartikel (refereegranskat)abstract
    • Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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