Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

Hum Mutat. 2019 Sep;40(9):1197-1201. doi: 10.1002/humu.23876. Epub 2019 Aug 26.


Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, and increasingly also in complex trait disease, with applications ranging from basic research to clinical decisions. Many computational impact prediction methods have been developed, yet the field lacks a clear consensus on their appropriate use and interpretation. The Critical Assessment of Genome Interpretation (CAGI, /'kā-jē/) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing with experimental and clinical data. CAGI has completed five editions with the goals of establishing the state of art in genome interpretation and of encouraging new methodological developments. This special issue ( comprises reports from CAGI, focusing on the fifth edition that culminated in a conference that took place 5 to 7 July 2018. CAGI5 was comprised of 14 challenges and engaged hundreds of participants from a dozen countries. This edition had a notable increase in splicing and expression regulatory variant challenges, while also continuing challenges on clinical genomics, as well as complex disease datasets and missense variants in diseases ranging from cancer to Pompe disease to schizophrenia. Full information about CAGI is at

Keywords: CAGI; Critical Assessment of Genome Interpretation; SNP; cancer genetics; genetic variation; genomics; variant impact predictors.

Publication types

  • Congress
  • Introductory Journal Article
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Congresses as Topic
  • Data Interpretation, Statistical
  • Genome, Human*
  • Genomics
  • Humans
  • Precision Medicine