We propose to organize two further Critical Assessment of Genome Interpretation (CAGI) conferences, in 2017 and 2018. The CAGI experiments and conferences are providing the primary independent assessment of the state of the art of variation interpretation. In the CAGI experiments participants are provided genetic variants and make blinded predictions of resulting molecular, cellular, organismal, or clinical phenotypes. The new experiments will build on extensive and informative results obtained in the first four rounds. Datasets will include rare disease, common diseases, and germline and somatic cancer variation, from both research and clinical sources. Data types will include complete genomes and exomes, as well as single base changes affecting coding sequence, gene expression, and RNA splicing. Independent assessors will evaluate the predictions against experimentally characterized phenotypes. A CAGI Conference is held at the end of each experiment. The specific goals of the conferences are: (1) to assess the quality of current computational methods for interpreting genomic data, and highlight innovations and progress; (2) to guide future research efforts in computational genome interpretation and build a strong community for collaboration and interaction; and (3) to disseminate results both amongst key members of the variant-phenotype prediction community at the meeting and to a broader audience via publication of results in peer-reviewed journals. The new CAGI experiments will continue the process already established over four rounds, starting in 2010 and with the latest meeting in March 2016. The 2016 experiment yielded 174 submissions on 11 challenges, by 37 groups from 13 countries. 57 people attended the meeting, and we are disseminating results via open access publications and conference presentations. Once again, the participating community was overwhelmingly of the opinion that this experiment is necessary and should be organized again on an ongoing basis. The organizers will continue to encourage the participation of women and underrepresented minorities, and broad participation of trainees and senior scientists at the CAGI conferences. Funding is requested for awarding 17 trainee fellowships for students and postdoctoral researchers to cover registration and approximately 2/3 of their other participation costs. In addition, we seek funding to subsidize registration and approximately half of meeting costs of the independent assessors, some data providers, and the organizers of the CAGI experiments.

Public Health Relevance

Genomic variation is responsible for numerous rare diseases, propensity for many common traits and cancers, drug response, and is a key characteristic of cancer development. At present, our ability to characterize genetic differences far exceeds our capacity to interpret it either for basic research understanding or for clinical diagnosis. The Critical Assessment of Genome Interpretation will provide an evaluation of the current state-of-the-art and help promote progress in understanding genomic variation.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Conference (R13)
Project #
2R13HG006650-04
Application #
9331880
Study Section
Genome Research Review Committee (GNOM-G)
Program Officer
Struewing, Jeffery P
Project Start
2011-09-01
Project End
2019-04-30
Budget Start
2017-06-13
Budget End
2018-04-30
Support Year
4
Fiscal Year
2017
Total Cost
$45,000
Indirect Cost
Name
University of California Berkeley
Department
Other Basic Sciences
Type
Schools of Earth Sciences/Natur
DUNS #
124726725
City
Berkeley
State
CA
Country
United States
Zip Code
94704
Niroula, Abhishek; Vihinen, Mauno (2017) PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned. Hum Mutat 38:1085-1091
Kreimer, Anat; Zeng, Haoyang; Edwards, Matthew D et al. (2017) Predicting gene expression in massively parallel reporter assays: A comparative study. Hum Mutat 38:1240-1250
Carraro, Marco; Minervini, Giovanni; Giollo, Manuel et al. (2017) Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI. Hum Mutat 38:1042-1050
Beer, Michael A (2017) Predicting enhancer activity and variant impact using gkm-SVM. Hum Mutat 38:1251-1258
Daneshjou, Roxana; Wang, Yanran; Bromberg, Yana et al. (2017) Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Hum Mutat 38:1182-1192
Cai, Binghuang; Li, Biao; Kiga, Nikki et al. (2017) Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges. Hum Mutat 38:1266-1276
Capriotti, Emidio; Martelli, Pier Luigi; Fariselli, Piero et al. (2017) Blind prediction of deleterious amino acid variations with SNPs&GO. Hum Mutat 38:1064-1071
Wang, Maggie Haitian; Chang, Billy; Sun, Rui et al. (2017) Stratified polygenic risk prediction model with application to CAGI bipolar disorder sequencing data. Hum Mutat 38:1235-1239
Giollo, Manuel; Jones, David T; Carraro, Marco et al. (2017) Crohn disease risk prediction-Best practices and pitfalls with exome data. Hum Mutat 38:1193-1200
Xu, Qifang; Tang, Qingling; Katsonis, Panagiotis et al. (2017) Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4. Hum Mutat 38:1123-1131

Showing the most recent 10 out of 25 publications