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                            Library & Multimedia
 Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits
NCJ Number
              307016
Journal

*Forensic Science International: Genetics* Volume: 50 Dated: January 2021
Author(s)
          Maria-Alexandra Katsara;
                        Wojciech Branicki;
                        Ewelina Pośpiech;
                        Pirro Hysi;
                        Susan Walsh;
                        Manfred Kayser;
                        Michael Nothnagel
Date Published

January 2021
Length
              13 pages
Annotation
              This article presents research into the prediction of appearance traits (i.e., eye, hair, and skin color, as well as hair structure and freckles) using trait prevalence-informed priors in Bayesian models.
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, the authors assessed the impact of using trait prevalence-informed priors on the prediction performance in Bayesian models for eye, hair and skin color as well as hair structure and freckles in comparison to the respective prior-free models. Those prior-free models were either similarly defined either very close to the already established ones by using a reduced predictive marker set. However, these differences in the number of the predictive markers should not affect significantly our main outcomes. The authors observed that such priors often had a strong effect on the prediction performance, but to varying degrees between different traits and also different trait categories, with some categories barely showing an effect. While the authors found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, the authors’ analyses also showed that misspecification of those prior values often severely diminished the accuracy compared to the respective prior-free approach. This emphasizes the importance of accurate specification of prevalence-informed priors in Bayesian prediction modeling of appearance traits. However, the existing literature knowledge on spatial prevalence is sparse for most appearance traits, including those investigated here. Due to the limitations in appearance trait prevalence knowledge, the authors’ results render the use of trait prevalence-informed priors in DNA-based appearance trait prediction currently infeasible. (Published Abstract Provided)
      
  
          Additional Details
Grant Number(s)
        2018-DU-BX-0219


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Ensuring standards and enhancing the field of predictive biometrics using a globally-diverse genotype-phenotype database
    
  
  
Date Published: January 1, 2021
    
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