New genomic data hold the promise of revolutionizing our understanding and treatment of human disease, and hence of greatly influencing clinical practice. Multiple barriers stand between the acquisition of the data and realizing these and other benefits. In particular, powerful and well-characterized computational methods for deducing the disease relevant phenotypic impact of genomic variants are needed. Over fifty such methods already exist, but currently, even though some are already deployed in clinical practice, we do not know how well these perform on relevant genome interpretation tasks. Further, it is already clear that new and more sophisticated approaches must be developed to fully meet the new challenges. The Center for Critical Assessment of Genome Interpretation (C-CAGI) will address these needs, through ongoing objective evaluation of the state of the art in relating genetic information to phenotype, particularly the relationship between human genetic variation and health. These goals are embraced by three specific aims: 1. Assess the quality of current computational methods for interpreting genomic variation data, and highlight innovations & progress. Building on successful initial experiments in 2010, 2011 and 2013, C-CAGI will conduct community-wide experiments in which participants make bona fide blinded predictions of disease related phenotypes on the basis of genomic data. These are evaluated by independent assessors with access to the correct answers, to determine how well methods work both relatively and absolutely. These assessments will establish the state-of-the art and advance the field. 2. Guide future research efforts in computational genome interpretation and build a strong community for collaboration and interaction. C-CAGI aims to engage and expand the community of researchers interpreting the phenotypic impact of genomic variation with CAGI workshops, hackathons, tutorials, and other mechanisms. We hope to use CAGI to spur and recognize innovative approaches to the breadth of practical and clinical research. The C-CAGI will also encourage and commission experimental studies necessary for focused testing of the computational methods. C-CAGI operates on a robust ethical foundation in using human research participant data, supported by the CAGI Ethics Forum. 3. Broadly disseminate the results and conclusions from the CAGI experiments and analysis. C-CAGI aims to be the central resource for information on interpretation of genomic variation. This dissemination to the broader scientific and clinical community will be using its publications, best practice guides, and web resources, and through presentations, tutorials, and workshops at international meetings.

Public Health Relevance

Genomic variation is responsible for numerous rare diseases, for propensity for many common traits and diseases, for drug response, and is a key characteristic of cancer evolution. At present, our ability to characterize genetic differences far exceeds our capacity to interpret them either for basic research understanding or for clinical application. The Center for Critical Assessment of Genome Interpretation, operating on robust ethical foundations, will provide an evaluation of the current state of the art and help promote progress in understanding the impact of genomic variation.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Biotechnology Resource Cooperative Agreements (U41)
Project #
1U41HG007346-01A1
Application #
8883057
Study Section
National Human Genome Research Institute Initial Review Group (GNOM)
Program Officer
Struewing, Jeffery P
Project Start
2015-05-13
Project End
2018-04-30
Budget Start
2015-05-13
Budget End
2016-04-30
Support Year
1
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of California Berkeley
Department
Other Basic Sciences
Type
Earth Sciences/Resources
DUNS #
124726725
City
Berkeley
State
CA
Country
United States
Zip Code
94704
Oetting, William S; BĂ©roud, Christophe; Brenner, Steven E et al. (2018) Methods and tools for assessing the impact of genetic variations: The 2017 Annual Scientific Meeting of the Human Genome Variation Society. Hum Mutat 39:454-458
Dyke, Stephanie O M; Linden, Mikael; Lappalainen, Ilkka et al. (2018) Registered access: authorizing data access. Eur J Hum Genet 26:1721-1731
Lu, Jacqueline G; Bishop, Juliet; Cheyette, Sarah et al. (2018) A novel PRRT2 pathogenic variant in a family with paroxysmal kinesigenic dyskinesia and benign familial infantile seizures. Cold Spring Harb Mol Case Stud 4:
Crawford, Dana C; Morgan, Alexander A; Denny, Joshua C et al. (2018) PRECISION MEDICINE: FROM DIPLOTYPES TO DISPARITIES TOWARDS IMPROVED HEALTH AND THERAPIES. Pac Symp Biocomput 23:389-399
Morgan, Alexander A; Crawford, Dana C; Denny, Josh C et al. (2017) PRECISION MEDICINE: DATA AND DISCOVERY FOR IMPROVED HEALTH AND THERAPY. Pac Symp Biocomput 22:348-355
Zhang, Jing; Kinch, Lisa N; Cong, Qian et al. (2017) Assessing predictions of fitness effects of missense mutations in SUMO-conjugating enzyme UBE2I. Hum Mutat 38:1051-1063
Hoskins, Roger A; Repo, Susanna; Barsky, Daniel et al. (2017) Reports from CAGI: The Critical Assessment of Genome Interpretation. Hum Mutat 38:1039-1041
Katsonis, Panagiotis; Lichtarge, Olivier (2017) Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests. Hum Mutat 38:1072-1084
Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek et al. (2017) DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning. Hum Mutat 38:1217-1224
Yin, Yizhou; Kundu, Kunal; Pal, Lipika R et al. (2017) Ensemble variant interpretation methods to predict enzyme activity and assign pathogenicity in the CAGI4 NAGLU (Human N-acetyl-glucosaminidase) and UBE2I (Human SUMO-ligase) challenges. Hum Mutat 38:1109-1122

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