GenVue Discovery
Upload raw data from AncestryDNA, 23andMe, or upload a Whole Genome Sequencing (WGS/WES) file in .vcf.gz or .vcf format (1 GB max file size) to learn about conditions, drug/chemical response, rare mutations, and more. If you have multiple VCF files, SNP and Indel files are compatible. No information about uploaded files are saved or shared. Uploaded files are de-identified on our server and deleted within 24 hours.
Yes. Your data is not stored on the server for more than 24 hours and is not shared with anyone. In most cases, we can't even see your data as uploaded genetic data is usually deleted immediately after processing. No data (including the downloadable archives of data and CSV files) are stored on the server for more than 24 hours. We may occasionally look at data for the purposes of improving this service. Look at our Privacy Policy to learn more.
23andMe, AncestryDNA, and Whole Genome/Exome Sequence (WGS/WES) VCF data is supported. While we currently only allow data from the largest consumer genomic companies, virtually any WGS/WES data is compatible with this service as long as the data is aligned to hg19/GRCh37 or hg38/GRCh38.
Generally speaking, the accuracy of the reports reflects the accuracy of raw data in combination with the accuracy of third party databases, the accuracy of our algorithms that interpet this data, and the accuracy of our filtering. While consumer genomic data definitely has its share of accuracy issues, accuracy is much better than some of the news articles makes it out to be. If you go by reprodicubility measures, the reproducibility of BeadChip Arrays is around 99.99% or greater. Reproducibility is not the only factor in determining accuracy, but high reproducibility is very important for high accuracy. Despite the reproducibility, consumer genome data has a lot of miscalls, so a lot of filtering has to be done on our end.
The accuracy of Whole Genome and Whole Exome Sequencing (WGS/WES) data can vary. Accuracy of non-low-pass WGS/WES data should exceed the accuracy of any consumer genomic BeadChip array as long as the provider of the VCF uses a good variant caller with good filtering strategies. Data with higher depth (30x and greater) is going to be more accurate and more represenative of the whole genome. Furthermore, the accuracy of data may not be very good if you or your provider are using poor filtering strategies. By default, we trust PASS filters on VCF files. If a VCF is not filtered or PASS filters are not present, we use a very basic universal filter that relies on QUAL (Quality) and DP (Depth), which is better than nothing. If you want to run all of your data through GenVue Discovery regardless of quality, you or your provider must put PASS filters on all variants in your VCF file.
When interpreting variants, we may swap the reference and alternate allele if the reference allele represents the risk allele. This can be especially true for Drug Response section (note: We currently aren't doing this as of writing this, but we may do this at any point). There are no guarantees of report accuracy or lack of programmatical errors in interpretation of the data.
Consumer genomic data (23andMe and Ancestry) represents a very small amount of the genome — roughly 0.02%, while Whole Genome and Whole Exome Sequencing (WGS/WES) represents (nearly) 100% of the genome and exome respectively. While the consumer genomic companies use a customized BeadChip array and try to select SNPs that are more important and/or have more variance, each SNP chip version will produce raw data that has a different set of SNPs. And this set of SNPs can vary between companies. Not every chip will have all the same variants.
While Whole Genome and Whole Exome sequencing (WGS/WES) don't have these same set of problems, they have problems of their own. VCF files typically don't report reference variants to keep file sizes compact (hundreds of megabytes instead of tens of gigabytes). For some Genetic Genie Panel apps, this creates some challenges. However, since we don't look at reference variants in GenVue Discovery, this particular issue isn't a concern for this app.
Yes. While hg38/GRCh38 has many more variants than GRCh37, GRCh38 still has problems when it comes to mapping some important medical-related genes. However, hard to map regions in GRCh37 may map well in GRCh38. For these reasons we currently recommend using hg19/GRCh37 and hg38/GRCh38 VCF files if possible for higher sensitivity. Occasionally, an important variant may be missing in one file, but will show up in another VCF file using a different reference genome.
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