1. Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits
- Author
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Katsara, M.-A. (Maria-Alexandra), Branicki, W. (Wojciech), Pośpiech, E. (Ewelina), Hysi, P. (Pirro), Walsh, S. (Susan), Kayser, M. (Manfred), Nothnagel, M. (Michael), Katsara, M.-A. (Maria-Alexandra), Branicki, W. (Wojciech), Pośpiech, E. (Ewelina), Hysi, P. (Pirro), Walsh, S. (Susan), Kayser, M. (Manfred), and Nothnagel, M. (Michael)
- Abstract
The prediction of appearance traits by use of solely genetic information has become an established approach and a number of statistical prediction models have already been developed for this purpose. However, given limited knowledge on appearance genetics, currently available models are incomplete and do not include all causal genetic variants as predictors. Therefore such prediction models may benefit from the inclusion of additional information that acts as a proxy for this unknown genetic background. Use of priors, possibly informed by trait category prevalence values in biogeographic ancestry groups, in a Bayesian framework may thus improve the prediction accuracy of previously predicted externally visible characteristics, but has not been investigated as of yet. In this study, we assessed the impact of using trait prevalence-informed priors on the prediction pe
- Published
- 2021
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