Chen-Hsin Yu, Yizhou Yin, Christopher Douville, Julian Gough, Silvio C.E. Tosatto, Biao Li, Melissa Cline, Tychele Turner, Timothy Bergquist, Sean D. Mooney, Lipika R. Pal, Noushin Niknafs, Manuel Giollo, Rachel Karchin, Roger A. Hoskins, Violeta Beleva-Guthrie, Hui Ting Grace Yeo, Emanuela Leonardi, Mark Diekhans, Yun-Ching Chen, Binghuang Cai, Rohit Bhattacharya, Mario Stanke, George Church, Dewey Kim, John Moult, Jason Bobe, Chen Cao, Hannah Carter, Steven E. Brenner, Susanna Repo, Carlo Ferrari, Nikki Kiga, Jan Zaucha, Janita Thusberg, Madeleine Ball, Marco Carraro, Jean Fan, Sohini Sengupta, Collin Tokheim
The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.
PGP provides unrestricted access to genomes of individuals and their associated phenotypes. The CAGI PGP challenge is to predict whether an individual had a particular trait or phenotype profile based on their whole genome. Assessment results show prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within general population, while matching genomes to trait profiles relies heavily upon a small number of common traits.
