Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges

Binghuang Cai, Biao Li, Nikki Kiga, Janita Thusberg, Timothy Bergquist, Yun Ching Chen, Noushin Niknafs, Hannah Carter, Collin Tokheim, Violeta Beleva-Guthrie, Christopher Douville, Rohit Bhattacharya, Hui Ting Grace Yeo, Jean Fan, Sohini Sengupta, Dewey Kim, Melissa Cline, Tychele Turner, Mark Diekhans, Jan ZauchaLipika R. Pal, Chen Cao, Chen Hsin Yu, Yizhou Yin, Marco Carraro, Manuel Giollo, Carlo Ferrari, Emanuela Leonardi, Silvio C.E. Tosatto, Jason Bobe, Madeleine Ball, Roger A. Hoskins, Susanna Repo, George Church, Steven E. Brenner, John Moult, Julian Gough, Mario Stanke, Rachel Karchin, Sean D. Mooney

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1266-1276
Number of pages11
JournalHuman mutation
Volume38
Issue number9
DOIs
StatePublished - Sep 2017
Externally publishedYes

Keywords

  • biomedical informatics
  • community challenge
  • critical assessment
  • genome
  • genome interpretation
  • open consent
  • personal genome project (PGP)
  • phenotype

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