Wang J, Al-Ouran R, Hu Y, Kim SY, Wan YW, Wangler MF, Yamamoto S, Chao HT, UDN Consortium, Comjean A, Mohr SE, Perrimon N, Liu Z, Bellen HJ (2017) MARRVEL: integration of human and model organism genetic resources to facilitate functional annotation of the human genome. American Journal of Human Genetics. doi:10.1016/j.ajhg.2017.04.010 PMID:28502612
MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) aims to facilitate the use of public genetic resources to prioritize rare human gene variants for study in model organisms. To facilitate the search process and gather all the data in a simple display we extract data from human data bases (OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER) for efficient variant prioritization. The protein sequences for eight organisms (S. cerevisiae, S. pombe, C. elegans, D. melanogaster, D. rerio, M. musculus, R. norvegicus, and H. sapiens) are aligned with highlighted protein domain information via collaboration with DIOPT. The key biological and genetic features are then extracted from existing model organism databases (SGD, PomBase, WormBase, FlyBase, ZFIN, MGI, and RGD).
As whole exome and genome sequencing are incorporated into personal health care, we are faced with an abundance of rare variants of unknown function. The lack of in vivo functional studies of variants further increases the difficulty in interpreting sequencing data and results in on average 30% genetic diagnostic rate. To understand the impact of these human variants and to increase the rate of diagnosis, it is critical to gather knowledge about the gene and the variant that helps us determine the significance of the findings. This information can be found in human genetic data sets as well as molecular, biological, and phenotypic data generated in a variety of genetic model organisms. Once this information is gathered and analyzed, it sets the stage for diagnostic interpretation and in depth studies of novel pathogenic mechanisms.
Patients with undiagnosed diseases with possible underlying genetic etiology are increasingly being sent for Whole Exome Sequencing or Whole Genome Sequencing. The result of the sequencing can produce a long list of possible candidate variants. Starting from a candidate human variant that may be disease causing, MARRVEL allows simultaneous collection of data from multiple sources that are used to determine how likely a variant can cause a rare genetic disease. Furthermore, we aim to guide the variant analysis further by transitioning to model organisms. In collaboration with Nobert Perrimon’s lab, we examine the conservation of the specific variant of interest in homologs/orthologs across model organisms and provide a concise summary of what is known about these genes. This is an important process to select the appropriate model organism to study candidate genes and variants.
In order to most efficiently utilize limited time and resources, MARRVEL aggregates several key public resources used to prioritize how likely a variant may be pathogenic. These resources become especially valuable for discovering rare genetic disease genes and together comprise a rich source of information for new disease gene discovery. The next stage of MARRVEL’s analysis is to curate information available for candidate genes and variants across multiple model organisms to evaluate conservation and assess what is already known about the homologous genes in model organisms.
For MARRVEL’s first set of data describing variants of interest, we selected the following 5 core public human genetics databases: OMIM, ExAC, ClinVar, Geno2MP, DGV, DECIPHER. These databases are useful for determining allele frequency of the variant of interest and if individuals with the variant exhibit similar phenotypes as the patient of interest. Additional databases will be added as they become available. In our interface, we collect and curated the critical information used for variant prioritization.
MARRVEL’s second set of data facilitates variant analysis in model organisms by providing known functional data and pursue further gene function annotation. In collaboration with Nobert Perrimon’s team, we have expanded the tools to: (1) Identify potential orthologs in 6 model organisms (budding yeast, fission yeast, worm, fly, zebrafish, mouse, and rat) via DIOPT (DRSC Integrative Ortholog Prediction Tool), (2) align model organism protein sequences and annotate protein domains and amino acid change of interest for conservation analysis, and (3) provide experimental evidence supported gene ontology and tissue expression pattern.
Julia Wang
Rami Al-Ouran
Yanhui (Claire) Hu
Seon Young Kim
Dongxue Mao
Sasidhar Pasupuleti
Naveen Manoharan
Ying-Wooi Wan
Michael Wangler
Shinya Yamamoto
Hsiao-Tuan Chao
Aram Comjean
Stephanie Mohr
Norbert Perrimon
Hugo Bellen
Zhandong Liu
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The authors would like to thank the Exome Aggregation Consortium and the groups that provided
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The authors would like to thank the University of Washington Center for Mendelian Genomics and all
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This study makes use of data generated by the DECIPHER community. A full list of centres who contributed to the generation of the data is available from http://decipher.sanger.ac.uk and via email from decipher@sanger.ac.uk. Funding for the project was provided by the Wellcome Trust.
We thank the expert advice and feedback from:
Undiagnosed Diseases Network Model Organism Working Group and Coordinating Center
Jim Lupski, Richard Gibbs, Zeynep Akdemir, John Seavitt, George Eisenhoffer, Swathi Arur, and
Grezegorz Ira
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Undiagnosed Diseases Network Model Organism Working Group and Coordinating Center
Jim Lupski, Richard Gibbs, Zeynep Akdemir, John Seavitt, George Eisenhoffer, Swathi Arur, and Grzegorz Ira
