Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR

Journal name:
Nature Protocols
Volume:
10,
Pages:
1556–1566
Year published:
DOI:
doi:10.1038/nprot.2015.105
Published online

Abstract

Recent developments in sequencing techniques have enabled rapid and high-throughput generation of sequence data, democratizing the ability to compile information on large amounts of genetic variations in individual laboratories. However, there is a growing gap between the generation of raw sequencing data and the extraction of meaningful biological information. Here, we describe a protocol to use the ANNOVAR (ANNOtate VARiation) software to facilitate fast and easy variant annotations, including gene-based, region-based and filter-based annotations on a variant call format (VCF) file generated from human genomes. We further describe a protocol for gene-based annotation of a newly sequenced nonhuman species. Finally, we describe how to use a user-friendly and easily accessible web server called wANNOVAR to prioritize candidate genes for a Mendelian disease. The variant annotation protocols take 5–30 min of computer time, depending on the size of the variant file, and 5–10 min of hands-on time. In summary, through the command-line tool and the web server, these protocols provide a convenient means to analyze genetic variants generated in humans and other species.

At a glance

Figures

  1. The three different types of annotations supported by ANNOVAR are gene-based, region-based and filter-based annotations.
    Figure 1: The three different types of annotations supported by ANNOVAR are gene-based, region-based and filter-based annotations.

    Different annotations focus on different aspects of each variant: gene-based annotation tells its relationship and functional impact on known genes; region-based annotation tells its relationship with different specific genomic regions, such as whether it falls within a known conserved genomic region; and filter-based annotation gives a variety of information on this variant, such as population frequency in different populations and various types of variant-deleteriousness prediction scores, which can be used to filter the common and probably nondeleterious variants.

  2. Screenshot of wANNOVAR, including the general steps to upload and prioritize variants.
    Figure 2: Screenshot of wANNOVAR, including the general steps to upload and prioritize variants.

    Please follow the steps (1–9) in the picture. If you want to quickly start the job with default parameters, please directly click submit (9) after the variant file is uploaded.

  3. Screenshot of the wANNOVAR result page.
    Figure 3: Screenshot of the wANNOVAR result page.

    (a) The basic information section includes the submission ID, submission information and the annotated variant list, which can be accessed by clicking 'View', or it can be downloaded by clicking 'CSV file' or 'TXT file'. (b) The additional information section includes the filtered variant files, the phenotype-based gene prioritization and annotations. If you have selected a disease model, the download links of the results after each filtering step will be shown. For example, Step 1 identifies missense, nonsense and splicing variants from the input variant list, and it provides the VCF file through the 'download' link. If you have entered any disease or phenotype terms, the prioritized gene list can be retrieved from the 'Result Gene List' link and the network visualization can be retrieved by clicking 'Show'.

  4. The expected results for discovering candidate genes of the /`hemolytic anemia/' example in the Phenolyzer website.
    Supplementary Fig. 1: The expected results for discovering candidate genes of the ‘hemolytic anemia’ example in the Phenolyzer website.

    Each ball represents one of the top 50 ranked genes. The larger the ball, the higher the ranking. The blue balls represent disease genes reported before and the yellow ones represent predicted disease genes. For detailed explanations on each color and shape, and on how the algorithm works to find disease genes, please visit http://phenolyzer.usc.edu/FAQ.php

  5. Explanation of each column in the wANNOVAR web view.
    Supplementary Fig. 2: Explanation of each column in the wANNOVAR web view.

    This is a sample output with default parameters. The first 5 columns represent the original information on the input variants. The following 5 columns give gene-based annotations on each variant. The following columns give region-based and filter-based annotations on each variant.

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Author information

Affiliations

  1. Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California, USA.

    • Hui Yang &
    • Kai Wang
  2. Neuroscience Graduate Program, University of Southern California, Los Angeles, California, USA.

    • Hui Yang
  3. Department of Psychiatry, University of Southern California, Los Angeles, California, USA.

    • Kai Wang
  4. Department of Preventive Medicine, Division of Bioinformatics, University of Southern California, Los Angeles, California, USA.

    • Kai Wang

Contributions

H.Y. and K.W. drafted and revised this manuscript.

Competing financial interests

K.W. is a shareholder and board member of Tute Genomics.

Corresponding author

Correspondence to:

Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: The expected results for discovering candidate genes of the ‘hemolytic anemia’ example in the Phenolyzer website. (96 KB)

    Each ball represents one of the top 50 ranked genes. The larger the ball, the higher the ranking. The blue balls represent disease genes reported before and the yellow ones represent predicted disease genes. For detailed explanations on each color and shape, and on how the algorithm works to find disease genes, please visit http://phenolyzer.usc.edu/FAQ.php

  2. Supplementary Figure 2: Explanation of each column in the wANNOVAR web view. (281 KB)

    This is a sample output with default parameters. The first 5 columns represent the original information on the input variants. The following 5 columns give gene-based annotations on each variant. The following columns give region-based and filter-based annotations on each variant.

PDF files

  1. Supplementary Text and Figures (861 KB)

    Supplementary Figures 1 and 2, Supplementary Tables 1–3

Excel files

  1. Supplementary Data (464 KB)

    ANNOVAR and VEP comparison results

Additional data