Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits

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 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. We 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 we found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, our 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, our results render the use of trait prevalence-informed priors in DNAbased appearance trait prediction currently infeasible.

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019–2022

“…The VISAGE ( Vis ible A ttributes Through Ge nomics) Consortium, which consists of 13 partners from academic, police, and justice institutions in 8 European countries, has established new scientific knowledge and developed and tested prototype tools for DNA analysis and statistical interpretation as well as conducted education for stakeholders. In the 2019 to 2022 time window of this review, this concerted effort produced 45 one review article [ 417 ], 22 original research publications [ 337 , [420] , [421] , [422] , [423] , [424] , [425] , [426] , [427] , [428] , [429] , [430] , [431] , [432] , [433] , [434] , [435] , [436] , [437] , [438] , [439] , [440] ], and three reports [ [441] , [442] , [443] ].…”

Advancement in Human Face Prediction Using DNA

“…Some available 3D face databases include FaceBase, Stirling ESRC 3D Face Database, Bosphorus Database, etc. [207][208][209][210]. In 2017, scientists developed the VISible Attributes Through GEnomics (VISAGE) Consortium to validate and enhance the application of FDP in forensic cases.…”