ABSTRACT
Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.
Subject(s)
Confidentiality , DNA Fingerprinting , Models, Genetic , Phenotype , Whole Genome Sequencing , Adult , Age Factors , Algorithms , Body Size , Cohort Studies , Data Anonymization , Female , Humans , Male , Middle Aged , Pigmentation/genetics , Young AdultABSTRACT
We present a new rigging and skinning method which uses a database of partial rigs extracted from a set of source characters. Given a target mesh and a set of joint locations, our system can automatically scan through the database to find the best-fitting body parts, tailor them to match the target mesh, and transfer their skinning information onto the new character. For the cases where our automatic procedure fails, we provide an intuitive set of tools to fix the problems. When used fully automatically, the system can generate results of much higher quality than a standard smooth bind, and with some user interaction, it can create rigs approaching the quality of artist-created manual rigs in a small fraction of the time.
